Dataset-On-Demand: Automatic View Search and Presentation for Data Discovery

Many data problems are solved when the right view of a combination of datasets is identified. Finding such a view is challenging because of the many tables spread across many databases, data lakes, and cloud storage in modern organizations. Finding relevant tables, and identifying how to combine them is a difficult and time-consuming process that hampers users' productivity. In this paper, we describe Dataset-On-Demand (DoD), a system that lets users specify the schema of the view they want, and have the system find views for them. With many underlying data sources, the number of resulting views for any given query is high, and the burden of choosing the right one is onerous to users. DoD uses a new method, 4C, to reduce the size of the view choice space for users. 4C classifies views into 4 classes: compatible views are exactly the same, contained views present a subsumption relationship, complementary views are unionable, and contradictory views have incompatible values that indicate fundamental differences between views. These 4 classes permit different presentation strategies to reduce the total number of views a user must consider. We evaluate DoD on two key metrics of interest: its ability to reduce the size of the choice space, and the end to end performance. DoD finds all views within minutes, and reduces the number of views presented to users by 2-10x.

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1 Introduction

Analysts of modern organizations need to answer questions that require combining data from multiple different data sources, such as data lakes, databases and even files in cloud storage. Example combinations of datasets, or views, include training datasets necessary to build ML models, or the information needed to answer a business question. Finding the desired view is hard for two reasons. First, one must find relevant tables among many hundreds or thousands of data sources in modern organizations. Second, one must understand how to combine the relevant tables to create the desired view. Most users do not know how to find or join the view they need, often resorting to asking colleagues. Even expert users find the process to be error-prone and time-consuming.

Figure 1: Example of multiplicity of candidate views

Query-by-example [qbe, examplestutorial] based approaches such as S4 [sfour], MWeaver [mweaver], and Exemplar [exemplarqueries] help non-expert users complete a partially fulfilled query view. The query view may consist of attributes, tuple values, or a combination of both. The systems identify a query that fulfills the query view. However, they suffer two limitations. First, they do not handle semantic ambiguity. Consider the example of Fig. 1. The desired view is the top-left relation, which requests employee and address. It is possible to assemble 4 different views that fulfill that specification by joining the Employees table with any of the other tables. Joining with Billing Address, which contains home address, however, is semantically different than joining with Staff-2019 or Staff-2020, which contain work address and refer to two different years. In this case, the multiplicity of views stems from semantic heterogeneity in the data, i.e., multiple possible mappings, a problem that has long been identified in the data integration community [schemamappingasquerydiscovery]. Second, these approaches assume knowledge of the right join attributes between each pair of tables (they assume the existence of a PKFK graph), so they always know the correct way of joining any pair of tables. Assuming on the existence of a PFKF graph is a strong assumption we do not make in this paper, because obtaining the right PKFK graph across many tables stored in different systems and databases is a notoriously hard problem [findpkfk1, findpkfk2]. Instead of making such an assumption, we concede that some join attributes will be wrong and deal with these cases. For example, in Fig. 1, the Employees table may be wrongly joined with Customers using EID and CID as join attributes. Although there is an inclusion dependency between those attributes (both are integers drawn from the same domain), it is nevertheless not a valid join path, and will hence lead to a spurious view. We do not aim to automatically detect these cases, but to identify them efficiently when presenting views to users so they can select the correct one.

The crux of the problem is that the number of views that fulfill a given query view is large due to semantic heterogeneity, erroneous join attributes, and other anomalies. As a consequence, users are overwhelmed with a large choice space, from where they have to select the view.

In this paper we introduce dataset-on-demand (DoD), a view discovery system that helps users identify a combination of datasets (a view) from many data sources. With DoD, users first declare the view they need by providing the attributes they wish to see in the result as well as (possibly partial) tuples (see the example query view in Fig. 1). Then, the system automatically finds a collection of views that fulfill the specification. With DoD, finding both the relevant tables and correct join attributes is treated as a single view selection problem. DoD works by finding all possible views that fulfill the specification, and then classifying those views into groups using a new method, called 4C, which dramatically reduces the size of the view choice space. As a result, users are only involved at the end of the process, and they choose the right view from a smaller choice space instead of from all the resulting views.

To reduce the size of the choice space, the 4C method first classifies all views that fulfill a query view into 4 category groups, and then a presentation strategy uses those 4 groups to summarize, combine, and prune views. The 4 category groups are: compatible, which means that views are identical; contained, which means that the values of one view subsume others; complementary, which means that the set difference of tuples of views is not empty; and contradictory, which means that certain tuples are incompatible across views. An example of contradictory views is shown in Fig. 1: the view that selects home address will contradict the view that selects work address, assuming the employee does not live in the work place. Once the views are classified into the 4 groups, a presentation strategy reduces the number of views users have to inspect. There are different presentation strategies that cater to different needs. For example, one presentation strategy may combine compatible views into a single view, show only the highest cardinality view among contained views, union complementary views, and ask users to pick the preferred view among contradictory views.

From a performance point of view, there are two operations that are expensive to compute. First, classifying views into the 4 classes becomes expensive when there are many input views. We propose a 4C-Chaining algorithm that minimizes the amount of computing resources needed by skipping computation when possible. Second, identifying all views involves searching for relevant tables among many data sources, and obtaining all possible ways to join and materialize them, which potentially involves executing a query across many data sources. We build an ad-hoc processing engine to perform off-core joins when tables are part of different data sources, e.g., a database and a CSV file in cloud blob storage. We build DoD on top of Aurum [aurum] to help with the search for relevant tables and join paths.

To evaluate DoD we focus on two key metrics: reduction of the size of the view choice space, and its end-to-end performance. We show that with 4C we can reduce the size of the view choice space by 2-10X. We also demonstrate the end to end performance of DoD, which can find and present views automatically within minutes and in many cases within a few seconds.

2 Background

In this section we present the problem dataset on demand solves (Section 2.1) and how we evaluate it. We also describe Aurum, on which we build DoD in Section 2.2. The overall architecture of DoD is shown in Fig. 2.

Definitions. A query view, , consists of a set of attributes, , and, optionally, a set of tuples, . A tuple is a set of values, , drawn from the attributes’ domain. If contains tuples, these can have values for only a subset of . Each attribute and tuple value in a query view is called an attribute and value constraint, respectively.

A view candidate, , consists of a set of attributes and a set of tuples that contains the defined in .

2.1 The Dataset-on-Demand Problem

Consider a user who wants to know what is the address of every employee in a company. The user writes a query view with the relevant attributes, as shown in the example of Fig. 1. We do not assume the user knows the precise attribute names, but the DoD interface helps with identifying them using an autocomplete feature.

In addition to the schema, the user may add example tuples to the query view, such as ‘Raul CF’ in the example (see Fig. 1). We assume users may make mistakes when introducing example tuples, e.g., typos. In addition, data may be dirty and not match user’s input. As a consequence, we do not expect that values in the query view will appear exactly in the data.

View candidate search. Given , DoD finds a collection of select-project-join queries that, when executed lead to a collection of candidate views, . In Fig. 2, step 1, the user submits a query view to DoD, which produces (step 2) output views. In practice, the number of views is often in the tens and more. The size of may be large for many different reasons. For example, because the attributes of the query view can appear in multiple different tables, e.g., ‘employee’ may appear in many tables that are irrelevant to this query. In addition, some of the inclusion dependencies used to produce the views may be wrong, leading to spurious views (such as joining with the table ‘Customers’ in the example of Fig. 1). Furthermore, some may fulfill the query view only partially, e.g., lacking some attributes. These views have to be considered as well, because if all views that fully fulfill the query view are wrong, the users can consider these partially fulfilled views. We explain this in Section 4.

Figure 2: Step 0: Aurum builds a discovery index from a collection of tables. Step 1: A user creates and submits a query view. Step 2: DoD searches for views based on the input query view. Step 3: The 4C method classifies all views into 4 groups (figure shows c1=compatible, and c3=complementary). Step 4: a presentation strategy uses the 4 groups to reduce the number of views. Step 5: the resulting views are presented to the user.

If DoD were to produce all , the choice space users would need to investigate would be very large: they would need to look at every view to choose the right one, or settle with the first one they find that seems appropriate, at risk of leaving better views unexplored. Although we can prioritize candidate views that fulfill the largest number of constraints, there are still many to consider manually, so we need a strategy to help users select the right candidate view.

View candidate selection. Given the candidate views, , 4C classifies them into 4 groups (step 3 of Fig. 2), and a presentation strategy is applied to reduce the total size of views that users must inspect. This is shown in step 4 in Fig. 2. Different presentation strategies cater to different needs. For example, with the 4c-summary strategy (explained later) users may interact with the system indicating ‘good’ views and ‘bad’ views, and this information can be used to further narrow down the search. Alternatively, DoD can avoid any user intervention with a multi-row relation presentation strategy we present later. We explain this in Section 3.

4C Example: Suppose the ‘employee-address’ query view above produces 16 views. Out of those, 14 fully fulfill the query view, and 2 others are partial, i.e., lack some attribute defined in the query view. Because we prefer views that fully fulfill the query view, we apply the 4C method with the 4C-summary (presented more in detail later) presentation strategy to the group with 14 views first. The 4C method finds that 8 of the 14 views are compatible, i.e., they are exactly the same. This can happen, for example, because equivalent join attributes were used to assemble the views, or because there are copies of the underlying tables in different databases. In this case, 4C-summary summarizes those 8 views into 1. Out of the remaining 6 views, 3 of them are contained in another one. This can happen, for example, because a view joined with tables that contained employees of engineering, and another view joined with tables that contained both engineering and sales. Instead of showing the 4 views, 4c-summary shows only the one that contains the other 3.

Suppose the previous two summarized views are complementary: each view has values that do not exist in the other one. This can happen due to the existence of different versions of the same table. 4c-summary unions these two views into 1. Finally, out of the remaining views, 4C finds a pair that is contradictory, that is, when looking at a particular key in one view, the row values are different than when looking at that same key in the other view. For example, an employee ‘Raul’ has a ‘Pie street’ address in one view, but a ‘Flea Av’ in another one. This can happen because the address attribute in one view referred to ‘work address’, while another one to ‘home address’. It is possible to ask users to resolve contradictions, and use their actions to further reduce the size of the choice space. Alternatively, another presentation strategy can assemble a single view with multiple values for employee ‘Raul’, so this contradiction can be dealt with downstream. At the end, instead of looking at 14 different views, 4C with 4c-summary reduces the size to a few choices, so it becomes much easier to select the right view (step 5 in Fig. 2).

Classifying all the views in into the 4 classes is an expensive process because it is necessary to compare all cells of each view to all other views. We show later the algorithms we introduce to make the process practical.

2.2 Data Discovery in Databases, Data Lakes, and Clouds with Aurum

In this section, we describe Aurum [aurum]

, an open source data discovery platform on top of which we build DoD.

Aurum overview. Aurum reads relations from data sources such as databases, lakes, and files in filesystems, and produces a discovery index (corresponds to step 0 in Fig. 2). The discovery index is logically a hypergraph where the nodes correspond to columns and edges represent relationships between columns, e.g., there is an inclusion dependency relationship. Hyperedges are used to indicate columns that are part of some hierarchy, e.g., they appear in the same relation. This is useful to identify tables from relevant columns. In addition to these edges, the discovery index also includes a full text search index of textual columns, as well as their attribute names.

Aurum provides a discovery API that uses the discovery index to answer complex discovery queries. For example, a discovery program can ask for nodes in the graph (columns) that contain a value, and then it can ask whether the tables that contain those columns (using hyperedges) can be joined together. We detail the relevant discovery API calls when describing DoD’s engine in the next section.

Why approximate inclusion dependencies suffice. State of the art approaches assume that a PKFK graph that indicates how to join every pair of tables is available [sfour, mweaver, exemplarqueries]. This is a strong assumption we do not make. Obtaining such a graph across multiple databases is extremely hard, and even if it existed, it would not be enough to solve the problem of semantically different join paths (see the example in Section 1).

DoD instead relies on inclusion dependencies, which Aurum finds automatically, and uses these as a proxy to understand which tables join with each other on which attributes. When using inclusion dependencies, we accept that some will not be correct attributes on which to join tables, and will therefore lead to wrong views. Nevertheless, this approach works because spurious views are classified and contrasted with other (correct) views as part of the 4C method, so users can choose the correct one.

Further, instead of using exact inclusion dependencies, DoD uses approximate inclusion dependencies. An approximate inclusion dependency between two columns exists when values of one column are approximately included in the other, and at least one of the two columns is almost unique. These relaxations help with two practical problems. First, it is sometimes useful to use approximate keys to combine datasets that would not be able to be combined otherwise, e.g., joining two tables on an attribute ‘Office Phone’ that is not a real key. This is true when tables come from different sources and nobody assigned a PKFK at design time. Second, because data is dirty, if we only considered exact keys, we’d ignore opportunities to combine datasets.

2.3 Performance Metrics

There are two key metrics to evaluate DoD: the ability of 4C to reduce the total number of views, and the end-to-end runtime. To measure the first, we apply the presentation strategy and measure how many views remain at the end.

Second, if finding and classifying all views takes hours, then the benefits of DoD in comparison to manually inspecting all views would be unclear, so it is important for the end to end runtime to be not more than a few minutes.

3 The 4C Method

We assume we have found all candidate views, , (step 2 in Fig. 2), and present the 4C method. 4C classifies views into four classes that we use to reduce the size of . We start formally defining the 4 categories in Section 3.1. Then we describe the algorithms (Section 3.2) and conclude the section describing different presentation strategies (Section 3.3).

3.1 Compatible, Contained, Complementary, Contradictory Views

A candidate view, contains a set of rows, , that can be retrieved with the function , . We can refer to a row, , with its key value, , which we can use to obtain the row; .

Compatible views. Two candidate views, and , are compatible if they have the same cardinality , and their set of tuples is exactly the same: and , where the symbol indicates the set difference operation.

Contained views. A view, , contains another view, , when and , that is, when all rows of are contained in .

Complementary views. Two views, and are complementary when and , that is, each view has rows not contained in the other view.

Contradictory views. A view, contradicts another view, , if a key value, , that exists in both views yields distinct rows, . Two rows are distinct when any value of any of their attributes is different.

Although the description above refers to two views, the 4C method classifies multiple views into each of the above classes. For example, many views can be compatible among themselves. One view may contain many other views. One single view can be complementary with respect to many others, and contradict many others as well. Similarly, a view can contain another view, be complementary to another one, compatible with another group and be in contradiction with other views on different rows, all at the same time. Next, we present the algorithm we use to classify the candidate views into the 4 above groups.

3.2 The 4C-Chasing Algorithm

Obvious methods to obtain the 4C classification of views perform poorly. For example, an algorithm could compare each cell of each view to all other views, but this would become too slow even when the number of views is low. One good baseline method hashes rows of views and use the set of hashes to quickly tell apart compatible and contained groups from complementary and contradictory. Unfortunately, this improved baseline still needs to use the per-cell comparison to distinguish between complementary and contradictory views, so its performance remains low.

We introduce the 4C-Chasing algorithm to speed up the classification. This algorithm is orders of magnitude faster than the baseline, as we show in the evaluation section.

3.2.1 Core 4C-Chasing

The main body of the algorithm is shown in Algorithm 1. We know each has a subset of the attributes defined in the query view. When the candidate view contains all attributes from the query view, we say their schema fully fulfills the query view. We prefer these views because they are closer to the definition given by the user. However, we must also consider candidate views with a schema that partially fulfills the query view (i.e., they contain only a subset of the attributes in the query view), in case all fully fulfilled views may be wrong.

Because 4C works on views with the same schema (attributes in this case), the first step is to separate the candidate views into groups according to their schema (line 1). Then, the algorithm runs the 4C classification for each group (see line 1), leading to the classification of views into the four groups: compatible (C1), contained (C2), complementary (C3), and contradictory, C4.

input :  , collection of candidate views
output :  , views classified per class and schema type
1 classify_per_schema();
2 for  do
3        find_candidate_keys();
4        identify_c1();
5        select_representative_c1();
6        identify_c2_and_candidate_c3c4();
7        identify_c3_and_c4();
8       
9return ;
Algorithm 1 Main 4C Algorithm
input :  , collection of candidate views
output :  , collection of lists, each list contains that are compatible
1 map();
2 for  do
3        hash();
4        add(, , );
5       
6 ;
7 return ;
Algorithm 2 identify_c1
input :  , collection of candidate views
output :  , list of tuples ; contains all
 , list of tuples ; are the sets of row indices in which and disagree
1 (map(), map());
2 for  do
3        (hash_rows(), hash_rows());
4        (set(), set());
5        if len() len() then
6               if  then
7                      add(, , );
8                     
9              
10       else
11               ;
12               ;
13               if  then
14                      indices(, );
15                      indices(, );
16                      add(, , , , );
17                     
18              
19       
20 (unpack(), unpack());
21 return ;
Algorithm 3 identify_c2_and_candidate_c3c4
input :  , list of tuples ; are the sets of row indices in which and disagree
output :  , list of tuples ; are the sets of row indices that are existing in (or ) and not the other view
 , list of tuples ; are the sets of key values (attribute ) in which presents a contradiction with respect to
1 build_graph();
2 seen set();
3 (set(), set());
4 while  do
5        .pop();
6        if  in seen then
7               continue;
8              
9        select(, );
10        select(, );
11        get_most_likely_key();
12        project(, );
13        project(, );
14        ;
15        .pop();
16        find_contrad(, , , );
17        mark_graph_node(, , );
18        add(, );
19        add(seen, );
20        ;
21        add(, );
22        add(seen, );
23        while contains_marked_nodes() do
24               obtain_marked_node();
25               for neighbor  do
26                      find_contrad(, neighbor, , );
27                      if  then
28                             mark_graph_node(, , );
29                             add(, );
30                             add(seen, );
31                            
32                     
33              
34       
35return ;
Algorithm 4 identify_c3_and_c4

The algorithm first identifies quickly compatible and contained groups of views. Then, it identifies complementary and contradictory views among the remaining ones.

Prepare views. In the main body of the loop, the algorithm first attaches metadata to each view, which consists of a value per attribute that indicates how likely the attribute is to be a key (line 1). This is computed as the ratio between the total number of distinct values in the attribute column and the total number of values in it. This metadata will be used later in the identify_c3_and_c4() function.

Identify compatible groups. The next step is to identify groups of views that are compatible (c1) with each other (line 1), which corresponds to Algorithm 2. The main idea is to hash each view (see line 2), and insert the view on a map that is keyed on the hash value. A view hash is the sum of its rows hashes, and a row hash is the sum of its cells hashes. With this hashing method, compatible views are guaranteed to have the same hash value, so this is an efficient way of identifying groups of compatible views fast.

Because all compatible views are identical, we select one only to continue the classification (line 1). The selected views are then given to a function in charge of identifying contained groups (c2), as well as groups that may be either complementary or contradictory (c34), see line 1.

Identify contained groups. The function to finding contained views is shown in Algorithm 3. For each pair of views (loop in line 3), the algorithm obtains the list and the set of hashes of each view respectively. The list will be used to identify indexes of rows (because it maintains the order), while the sets are used to quickly tell whether the views are contained or may be complementary. Lines 3 to 3 checks whether the hashes of one view are completely contained on the first, in which case the view is contained in the first and this is recorded in the variable . Note that because the loop enumerates pairs of views in both directions, it is not necessary to check for containment in both directions here.

If the views are not contained, the algorithm tries to decide if they are candidates for the complementary and contradictory group. This decision is done in lines 3 and 3. The algorithm computes the set difference of rows in both directions and checks whether it is non-empty in both cases. When it is, this indicates that each view has hashes not contained in the other view. This can be caused for two different reasons: either one view has rows that are non existent in the other (a case of complementarity), or the values of certain rows differ in some values, which would also lead to different hashes and indicate a contradiction. The algorithm returns the identified groups of contained views as well as those that may be complementary or contradictory.

3.2.2 Distinguising Complementary and Contradictory Views

The last part of the algorithm is to identify which pairs of tables in are complementary and which are contradictory. This corresponds to line 1 in the main body of algorithm 1. The body of this function is shown in more detail in Algorithm 4.

Checking whether views are complementary or contradictory is expensive. This is because it’s not possible to recover the view row values from the hash. Hence, it requires checking each row cell individually for all rows that are part of the set difference, and comparing it with all other views.

The insight that the 4C-chasing algorithm exploits is that if there exists a particular contradiction in one value between 2 views, it is likely that at least one of the views contradicts other views on the same value as well—for example, all views that were assembled using a particular join attribute between two tables will share the same contradictions. Therefore, instead of finding contradictions between each pair of views, we can use the contradictions we find to quickly test whether the same contradiction exists between other pairs too, i.e., we can chase the contradiction across the views in the group.

Separate complementary from contradictory. The algorithm first represents the pairs of views in an undirected graph (the chasing graph), see line 4. It then obtains a pair from the input pairs (line 4) and checks whether the different values (hashes) identified between this pair are due to complementarity or contradictions. For that, it first selects the relevant rows from each view (lines 4 and 4), and then it obtains the values for the key of each of those views. The key values are obtained by first identifying the most likely attribute key, , of those views (line 4), and then projecting that attribute on the selection (lines 4 and 4). This produces and , which are the key values that correspond to the distinct hashes of each view. At this point, the intersection of these two sets, , corresponds to the contradictory examples, while the rest of values correspond to the complementary ones (line 4). When the keys of two views are contradictory, the views will be classified as complementary because the set of hashes will be different, and the key intersection, , is empty.

Within the set of contradictory keys, it is still necessary to identify which cell value or values caused the contradiction. We want the specific cell values so we can test these with other views, and so we can include this fine-grained information with the metadata produced at the output. The contradictory cells are obtained in lines 4 to 4. Here we call mark_graph_node() on the graph as contradictory, and attach the key attribute, , (note that does not need to be the same as ) as well as the key values that produced a contradiction, .

Chasing graph. The last part of the algorithm uses the nodes of the graph which has been previously marked (due to a contradiction), and the already found contradiction to identify what other connected views contradict the node as well (lines 4 to 4). Without 4C-chasing, testing a pair involves performing operations in the worst case, with being the number of rows in the larger cardinality relation. This cost must then be paid for all-pairs of contradicting views, . In contrast, with the new algorithm, the cost of comparing each pair is only for cases where the contradictions are shared. The benefits of the algorithm are then directly related to how many contradictions are shared between views. Since contradictions are often shared across many views this algorithm is efficient.

Once finished, all are classified into 4 classes, which along with the query used to assemble the view, and the join graphs used, is given as input to a presentation strategy. Specialized users such as data stewards may want a fine-grain understanding of the SPJ used to assemble a specific view, or the indices of rows in which two views disagree, or about the attribute and value that correspond to the contradiction between a pair of views. They can use the information collected so far directly.

3.3 Presentation Strategies

The goal of a presentation strategy is to use the 4 classes into which the candidate views are classified in order to reduce the size of the view choice space. We explain next two such strategies:

4c-summary. This strategy reduces the view choice space by taking automatic actions for compatible, contained, and complementary views, and then asking a user to choose an option among contradictory views. This strategy was illustrated in the example in Section 2.1. It summarizes compatible groups with a representative view, and contained groups with the highest cardinality view. When views are complementary, the union of the views is shown. Finally, when views are contradictory, the system shows to users the view that contradicts the largest number of views. If users prefer one view the information is used to prune other views, otherwise, the next contradiction is shown. This strategy makes two key assumptions. First, that when views are contained, the highest cardinality one is preferred. Second, that when views are complementary, the union is preferred. These actions do not guarantee that the view the user wants is chosen, e.g., a user may prefer the view for a particular year (that may be contained in another view). However, users can always inspect the operations performed by the presentation strategy and recover the right view.

Multi-row views. This strategy does not require user intervention. A view is shown exactly only once for each schema group. When a view is both in a compatible, contained, and complementary group, it is unioned with its complementary views and shown once. When the view has contradictory views, the system performs a special union, where those rows that contradict each other become multi-rows. A multi-row is a tuple that for the same key has more than one value. This is not a valid relation, but it can be useful for users to have all the context to make their decision, and it can be useful to feed to certain downstream tasks—for example one that uses multi-rows to apply an entity resolution [entityresolution] algorithm.

Different presentation strategies cater different needs and present different tradeoffs, and more presentation strategies than the presented above are possible. While 4c-summary relies on minimal user intervention to deal with contradictory views, the multi-row strategy further automates the process. In all cases, all metadata collected is shown along with the view so users can build trust on the results.

4 DoD Engine

In this section we describe how DoD finds , which corresponds to step 2 in Fig. 2. We start by describing the search process in Section 4.1, as well as some details on the ad-hoc processing engine we use to join relations across sources (Section 4.2).

4.1 Finding Candidate Views

The search process finds from an input query view following the next stages:

4.1.1 Identify Candidate Tables

During the first step, DoD identifies the tables relevant to the input query view. A table is relevant to the query view if it fulfills an attribute constraint, a value constraint, or any number of the previous. A table fulfills an attribute constraint when it contains one attribute that appears in the query view. A table fulfills a value constraint when the table contains both an attribute and value constraint that were defined in the same column of the query view. Note that a table that only fulfills a value constraint, but not the corresponding attribute constraint, is not considered relevant: this is because values alone can appear in many different columns of the underlying datasets.

To identify underlying tables that fulfill an attribute constraint, DoD uses Aurum’s data discovery API. Given an attribute name, Aurum returns all columns (and the corresponding tables) that have an attribute name that matches (string equality) the input attribute name. Because users make use of Aurum’s autocomplete feature to assemble the attributes in the query view in the first place (see Section 2.1, exact string matching is sufficient to identify the tables.

To identify underlying tables that fulfill a value constraint, DoD uses Aurum again. It first finds the set of tables that fulfill the attribute constraint, as above. The results are the attribute constraint group. Then, DoD uses Aurum to search for the value constraint. Aurum returns columns that contain the value constraint using a full-text search index. This is the value constraint group. Finally, DoD takes the set intersection of both groups: the result is the set of tables that fulfill the value constraints.

The above procedure is performed for every attribute and value constraint in the query view. The output of this process is a list of relevant tables along with the query view constraints they satisfy. We call the union of relevant tables the candidate tables.

4.1.2 Find Candidate Groups

In this step, DoD wants to identify a group of tables that, if joined, would satisfy the query view definition. The ideal group would fulfill all the constraints in the query view, and would contain as few tables as possible—so fewer joins would be necessary. Finding this group is akin to solving a set cover problem over the candidate tables. This, however, is not sufficient, because it may be impossible to join the tables in that group.

Instead, DoD finds all subsets of tables from the candidate tables set that, together, fulfill as many constrains from the input query view as possible. Each subset of candidate tables is called a candidate group. Each candidate group, if materialized, would lead to a view that fulfills or partially fulfills the query view.

DoD performs the search of candidate groups using a procedure that caters to two preferences. First, groups that fulfill more constraints are preferred, as those are closer to the query view. Second, among candidate groups that fulfill the same number of constraints, smaller groups are preferred, because those involve fewer joins. These criteria is implemented in a greedy search process that is shown in detail in Algorithm 5 and works as follows:

input :  , collection of relevant tables and the constraints they fulfill
output :  , list of candidate groups
1 [];
2 c_group [];
3 c_group_constraints [];
4 stables sort_tables_number_constraints();
5 for refstables do
6        c_group.add(ref);
7        ref_contraints get_constraints(ref);
8        c_group_constraints ref_constraints;
9        if fulfill_query_view(c_group_constraints) then
10               .add(c_group);
11               continue;
12              
13       for t tables_after_ref(ref) do
14               constraints get_constraints(t);
15               new_constraints merge(ref_constraints, constraints);
16               if new_constraints then
17                      c_group.add(t);
18                      c_group_constraints.add(constraints);
19                      if fulfill_query_view(constraints) then
20                             c_group.add(t);
21                             .add(c_group);
22                            
23                     
24              
25       
26return ;
Algorithm 5 Find candidate groups

DoD sorts the candidate tables based on the number of constraints they fulfill (line 5), from higher to lower. The search process takes the first table of such list, called reference (ref in the algorithm) and adds it to a candidate group (lines 5-5). Then, it iterates over the tables in sort order (line 5) checking, at each step, whether the table fulfills constraints not covered by the reference table (line 5). When it does, the table is added to the candidate group. At this point, if all constraints are fulfilled by the candidate group, the group is stored and the process selects a new reference table. If not, then the search continues. Note that in some cases, a single table may fulfill all constraints (that’s why it’s necessary to check for query view fulfillment every time the candidate group is updated, such as in lines 5 and 5. This happens when the input query view corresponds to an existing table.

The output of this step is a collection of candidate groups (line 5), each with the set of constrains it fulfills.

4.1.3 Select Joinable Groups

The goal of this step is to select among the candidate groups, those that are joinable groups. A joinable group is a candidate group in which all the tables contained can be combined into one single table through the relational join operation. There may be multiple ways of joining the tables of a joinable group. This can happen if there exists more than one inclusion dependency between a pair of tables, or there are different intermediate tables that can be used to join two tables. Each strategy to join such tables is called a join graph. A joinable group, then, is a candidate group for which there is at least one existing join graph.

For each candidate group, DoD must find all join graphs that permit combining all tables. It is necessary to find all join graphs because some may be using a wrong inclusion dependency (leading to a wrong view) or one that is correct, but incompatible with the user’s intent—when there are different semantic views that fulfill the query view.

To find all the join graphs, DoD first finds all the join paths between every pair of tables in the group. Then, it combines join paths together to form join graphs that permit joining all tables in the group. DoD discards duplicate join graphs—those that join the same tables on the same attributes—as well as join graphs that do not join every table in the group.

In order to identify a join path between a pair of tables, DoD uses Aurum’s discovery API. Aurum returns all join paths between two tables, given a maximum number of hops, by querying its discovery index. Once all join graphs are identified, these are sorted based on the number of joins it is necessary to perform in each case. The output of the process is then the joinable groups, each with the fulfilled query view constraints.

Note on value constraints. A joinable group indicates that when the tables are joined together, the materialization will contain some of the attribute constraints defined in the query view. It cannot say, however, whether the materialization will contain a value constraint. This is because Aurum can identify whether there are inclusion dependencies between two tables, but it cannot answer whether the tables, when joined, would contain a particular value. Identifying what joinable groups will contain the desired specification at the output is the goal of the next step.

4.2 Select Materializable Groups

A materializable group is a joinable group that, when materialized, contains all the constraints of the joinable group, including the value constraints. Because Aurum cannot help with identifying materializable groups, DoD checks each joinable group individually, i.e., it performs the join and checks whether the value constraint appears at the output.

In this section, we discuss alternative ways of achieving that, and explain the alternative we chose. We then dedicate the rest of the section to specific optimizations we had to implement in DoD to increase its efficiency.

The need for querying across data sources calls for a federated query engine [haasfederation, bigdawg]. These engines receive queries expressed in some general language, e.g., SQL, and compile them into specific languages of the data sources. They have a couple of drawbacks. First, such engines require to configure a connector for each data source and code the logic to transform from the general query language to the specific data source one. Second, they need to interact with all the data sources in the enterprise, making these systems hard to deploy in practice.

Another alternative would be to assume all data is in a data lake with querying capacity, and run all queries there directly. However, in practice we have found that many times data must remain in certain data sources (e.g., for security or governance issues), or has not arrived to the lake yet. We only assume read access.

We implemented an ad-hoc query engine that does not suffer the deployment problems of federated systems and does not assume all data is in a central repository, or data lake. The engine is implemented using Python Pandas [pandas]. Its main advantage is that instead of relying on external querying capacity, it owns query processing. Despite its slower performance with respect to query engines of databases, the benefits of accessing seamlessly multiple sources are a good match for DoD: we do not need to modify any system and can control with fine-granularity how processing is done. We describe next a few technical details of the ad-hoc engine.

4.2.1 Materializing a Join Graph

A join graph contains the information necessary to join a set of N tables. The join graph nodes represent the tables, and an edge between two nodes indicates that the two tables can be joined together. Each node contains the attribute on which the table should be joined.

To materialize a join graph, joins are performed from the leaf nodes to inner ones. A leaf node is one that is connected to only another one. Every time a join is performed, its node and edge are removed. The procedure repeats N-1 times, until the resulting joined table is obtained.

On Join Ordering.

Choosing the right join ordering is crucial for good performance; this is one of the classic problems query optimizers aim to solve. Query optimizers rely on data statistics to estimate what join orderings are cheaper to compute. Unfortunately, in the scenario we consider, we cannot assume that the underlying tables will contain statistics, because they may proceed from different databases, and even file systems. Instead, DoD records the actual output cardinality as it executes joins, and relies on this information for future joins, as opposed to use the default

leaf to inner nodes join strategy. Since each query view may trigger multiple similar joins, this strategy is beneficial.

4.2.2 Expensive-To-Compute Joins

When materializing join graphs, DoD needs to deal with two practical problems: expensive joins and joins that have an output larger-than-memory. We explain how we deal with both problems next:

Sampling Joins. Certain joins are so expensive that they become the main computational bottleneck of the end-to-end view search. Instead of materializing the entire join, it is possible to join only a sample and then run the 4C method on the sample join. Because 4C runs on a sample, and not the full join, the view 4C class may change once the full join is materialized. Therefore, joining only a sample comes with the tradeoff that users may need to try a few more views before finding the right one.

The main challenge of joining on a sample is selecting the sample so that the resulting views can be fed into the 4C method. For example, a naive sampling strategy that selects uniformly from each join graph does not guarantee that tuples will overlap in the resulting views, because each sampling process is independent from the others. However, we need samples to overlap, or otherwise, the 4C method won’t find a good classification of views.

To address this problem, DoD employs a strategy that selects samples in a consistently random way. For every join, it uses a hash function on the join attribute of the larger table to map the values from all relations to a common hash space. The sample corresponds to the values with the top-K minimum (or maximum) hash values, with K indicating the sample size. Because this strategy is applied consistently across all join graphs, DoD makes sure that the resulting views can be compared with each other, unlike with independent uniform sampling. This strategy is reminiscent of conditional random sampling [crs], which has been used before to summarize and compare sets of elements. As a final note, the sampled views are not guaranteed to contain the value constraints defined in the input query view. If users want to see the value constraints in the candidate view, then it’s possible to specifically search for their key in the table and include those as part of the sample, although it’s not necessary for 4C to work.

Larger-than-Memory Joins. Sometimes it is necessary to materialize views that do not fit in memory, so users can analyze them offline. Because Pandas assumes that joins can be performed in memory, we implement a spill-to-disk strategy to overcome this limitation. However, because the spill-Join is significantly less efficient than the default in-memory join, we want to use it only when the join won’t fit in memory. The challenge is that without statistics it is hard to estimate when this is the case.

Our solution is inspired by dynamic query reoptimization [reoptimization]. When joining two tables, DoD selects a sample from one table and joins it with the other table. It measures the output cardinality of the partial join, and uses it to estimate the output cardinality of the whole table. With a measure of the memory required by each row, we can then make an informed decision on what join strategy to use: whether to use spill-Join, our external algorithm implementation (when it does not fit in memory) or directly in memory (when it does).

4.2.3 Caching Optimizations

There are 3 operations that are the main computational bottlenecks during the view search performed by DoD: determining whether a candidate group is joinable, determining whether a joinable group is materializable, and materializing the group.

We make extensive use of caches to amortize certain operations that are executed often. To determine whether a pair of tables can be joined with each other, we make a call to Aurum. Although this operation is relatively well optimized by the Aurum engine, it must be performed multiple times, so caching the responses helps with skipping computation. To determine whether a join graph is materializable, we need to execute a join query with predicates given by the cosnstraints in the query view. For a given pair of table-attributes, we can remember if they do not materialize. Then, before trying to materialize a new join graph, we first check using this cache that the join graph does not contain a non-materializable path (this strategy is equivalent to the optimizations performed by S4 [sfour]). Last, DoD reads tables from data sources in order to check whether they are materializable and, if so, to materialize them. Keeping an LRU cache with the read tables helps to avoid expensive IO.

5 Evaluation

In this section we evaluate DoD with respect to the two critical metrics of interest: capacity to reduce the size of the view choice space, and end-to-end performance. We organize the section around 3 key questions:

  • [leftmargin=1em, itemsep=.1em, parsep=.1em, topsep=.1em, partopsep=.1em]

  • Does DoD-4C reduce the size of the view choice space? The default view choice space size is the number of candidate views generated by a query view. We want to understand the reduction achieved by 4C.

  • Is the 4C-chasing algorithm fast enough to be practical? Classifying the candidate views into the 4 categories is an expensive process. The longer time it takes, the lower the benefits it yields. We compare with a baseline to demonstrate its efficiency and practicality.

  • Performance of DoD. In this section we conduct experiments to understand DoD’s end-to-end performance, the factors that contribute to its performance, studying its scalability, as well as the impact of each of the ad-hoc processing optimizations.

We start the section by presenting the setup and datasets used and then explore each of the questions above. We conclude the section with a summary of the results obtained.

5.1 Setup and Datasets

We conduct all experiments in a MacBook Pro with 8GB memory and a core i7 with 4 cores and 3GHz speed each. We use two real world datasets in our evaluation:

DWH: The MIT datawarehouse dataset consists of 160 tables that have been integrated from 116 different databases. The dataset is heterogeneous, with information concerning many different aspects of the institute, faculty, facilities, students, subjects, footage, etc. Every table’s size in this dataset is within 100MB.

CHE: This dataset consists of 113 tables from two popular public chemical databases, ChEMBL [chembl], and DrugCentral [drugcentral]. The databases contain some overlapping information, but their emphasis is different, so it is common to conduct integration tasks between them. Tables of this dataset are of varying sizes, with a few multi-gigabyte tables, others below 1GB, and others within 100MB.

We use Aurum to build a discovery index for each dataset. We only provide Aurum with a connector to the databases, and we do not give any information about PKFKs or similar: all information DoD uses is inferred by Aurum. We use a collection of 10 query views from the 2 datasets above, 5 queries from each dataset. Throughout the section, we check that at least one of the candidate views is a correct view of the DoD output; we have correct views for each of the 10 query views we use.

5.2 Does DoD-4C reduce human intervention?

The goal of 4C is to reduce the number of candidate views produced by the search process. In this experiment we first submit query views to DoD and obtain the number of candidate views generated. This number is shown in the ‘Original Views’ column of table 1 (for DWH, we only show the 3 query views that produced a large number of views). This is the size of the original view choice space: the number of views that users would need to consider without the 4C method. Using the original candidate views as input, we use the 4C method and measure the reduction of the view choice space. We have implemented two presentation strategies. With multi-row, we will always obtain 1 multi-row view per schema type, so the more interesting case, and the one for which we show results, is that of 4c-summary.

The results for 4c-summary are shown in the ‘4c-summary’ column of table 1. The ‘x(y)’ notation indicates the total number of views (x), and the number of interactions users must make (y), i.e., when choosing among contradictory views. 4c-summary reduces by several factors the number of views for both datasets.

Note that users would likely not inspect each of the original candidate views. Instead, they may select the first candidate view that seems to address their needs. This, however, may leave a better view uncovered. In contrast, 4C does not skip any candidate view, leading to a more exhaustive exploration.

Original views 4c-summary
DWH2 9 4
DWH4 99 3(4)
DWH5 102 11(11)
CHE1 12 2(2)
CHE2 27 3(4)
CHE3 50 14(5)
CHE4 54 2
CHE5 127 23(23)
Table 1: Original and reduced views

When does 4C work? 4C’s ability to reduce the size of the choice space does not depend on the number of original candidate views, but on the actual content of the views. Consider the column for DWH4 and DWH5, as well as CHE3 and CHE4. Although in both cases the original number of views is similar, the reduction is quite different.

To understand why this is the case, consider a pathological case in which each view is contradictory with respect to every other view, so every candidate view must be considered at the output. Or, consider the opposite case where all views are compatible with each other, and hence summarized into only 1, in which no candidate view needs to be considered. Most query views we have used, including the ones we use in this evaluation, contain a mix of the 4 groups. This is due to semantic differences and wrong inclusion dependencies.

Note on PKFK. Many candidate views are spurious because they were obtained using a wrong join path. If the real PKFK-graph was available, the number of the candidate views would be lower, but the PKFK-graph is rarely available. An important contribution of DoD is precisely not assuming its existence and still deal with those spurious views post-hoc.

5.3 Is 4C-Chasing fast enough?

4C’s ability to reduce the size of the view choice space is only beneficial as long as the reduction is achieved fast. If users had to wait for hours to retrieve the results, the benefits would be unclear.

To understand 4C-Chasing algorithm’s efficiency, we measure its runtime over the set of query views that yielded a high number of candidate views (we use those with low candidate views to understand its overhead later). Further, to justify the design of 4C-Chasing, we compare its runtime with a baseline implementation of 4C, called No-Chasing. No-Chasing hashes rows so it can classify compatible and contained views quickly, but it must perform the cell by cell comparison across views to identify complementary and contradictory ones, as explained in Section 3. A naiver strategy that inspects each row individually takes too long even when the number of candidate views is small.

Figure 3: Runtime comparison of 4C-Chasing vs No-Chasing for different number of views

The results of Fig. 3 show that with 4C-Chasing algorithm, we can summarize the views about 2 orders of magnitude faster than No-Chasing for most queries. Even the more modest improvements of CHE2 and CHE3 are of 38% and 84% respectively.

In summary, with 4C-Chasing it is possible to vastly reduce the view choice space in under 2 minutes, making it practical.

What about 4C’s overhead? 4C is not necessary when the number of candidate views is low, or when classifying them is very fast, e.g., most views are compatible. In both of these cases, we want to understand what’s the overhead of executing 4C and its impact on the end-to-end execution. To measure the overhead, we executed 4C-Chasing on query views that either produced few candidate views (DWH1, DWH2, CHE1) or that produced mostly compatible views, such as CHE4.

Figure 4: Both 4C-Chasing and No-Chasing overhead is negligible when the number of views is low

The results of Fig. 4 show that both 4C-Chasing’s and No-Chasing’s overhead is low: under 2 seconds in both cases. This means that it is possible to always execute 4C because its overhead on the end-to-end runtime is negligible.

5.4 Performance of DoD

The end-to-end performance of DoD does not depend on the 4C method only, but also on the view search process, which we evaluate here. For that, we measure the time it takes for DoD to generate all the candidate views from the input queries of both datasets. In the case of DWH, we fully materialize the views, while in the case of CHE, we use the consistent sampling strategy (see Section 4), because the underlying tables of the dataset are much larger and many joins would run out of memory otherwise.

Runtime
(s)
CG
P
JG
Join
graphs
MG
DWH1 0.27 3 1 0 0 2
DWH2 46.8 22 48 9 2140 2
DWH3 4.6 15 9 3 9 9
DWH4 139.1 15 12 12 572 99
DWH5 683 24 47 12 2148 102
CHE1 856 4 2 2 69 12
CHE2 15.9 9 14 2 38 27
CHE3 16.1 11 6 5 61 50
CHE4 249.1 5 2 2 79 54
CHE5 37.5 11 11 6 127 122
Table 2: Statistics about e2e view search process for both datasets. Candidate groups (CG), Pairs of tables (P), Joinable groups (JG), Materializable groups (MG)

Table 2 shows the end to end runtime (first column) of DoD for different queries (shown in the rows) as well as statistics about each execution. The total runtime depends on several aspects of the search process: the effort to understand if tables join (Does Join?), the effort to check whether the joinable tables are materializable (Does Materialize?), and the time to actually materialize views (Materialize). Runtime numbers, per operation are shown in Fig. 5 and Fig. 6. The figures also include a category Other, to represent time spent doing work that does not fit in the categories above. We analyze each cost next:

Does Join? The P column in the table indicates the pairs of tables for which the system must find join paths (see Section 4.1.3). This number depends on the number of candidate groups, as well as the number of tables per group. The higher the number of pairs of tables, the costlier the Does Join? operation is, as confirmed by the results in the table and figures. This effect is more clearly seen in the case of the DWH dataset, because there are more pairs of tables for which to find join paths.

Figure 5: Total runtime split by component and for different queries (1/2)
Figure 6: Total runtime split by component and for different queries of (2/2)

Does Materialize? The Join Graphs column of Table 2 indicates how many potential ways we can join the pairs of tables identified before. This number depends to some extent on the number of joinable groups found (column JG), but mostly on how connected the specific tables are within Aurum’s discovery index. For each joinable graph, DoD must check if the graph is materializable (see Section 4.2), so naturally, the higher the number of join graphs, the higher the cost of Does Materialize?.

Materialize. Finally, the MG column (Table 2), materializable groups, indicates how many of the materializable join graphs, if materialized, would lead to a view that satisfies the input query view. This variable helps with understanding how much of the runtime is dedicated to actually materialize the joins to produce the output candidate views. In turn, the cost of materialization depends on how many views to materialize, and how expensive each join is. For example, in the case of the DWH dataset, query DWH5 is dominated by the materialization time. Each join is quite cheap, but there are 102 joins to perform, which explains the relatively high runtime. In the case of the CHE dataset, runtime is dominated by the materialization step. This is using using the sampling strategy of Section 4, which is necessary for this dataset because the underlying tables are large. Without it, many of the joins run out of memory, and those that don’t, take (each) about 6-8x longer to complete. The reason for the bottleneck is that in order to obtain the sample, it is necessary to read the entire relation into memory first—Pandas does not allow to efficiently read a relation selectively.

5.4.1 Scalability

DoD is built on top of Aurum, and benefits from its scalability [aurum]. However, DoD uses other operations, such as those performed by the ad-hoc query engine to understand if a join graph is materializable, and to materialize it when it is. In this experiment, we want to understand whether DoD’s view search scales as the amount of work to perform grows. Ideally, the runtime depends linearly on the amount of work that is necessary to perform.

X1
(9)
X2
(36)
X4
(192)
# Pairs tables 5 38 172
# Join graphs 9 72 576
# Materializable groups 3 24 192
Table 3: Statistics for the scalability experiment

In order to conduct this experiment, we use the DWH2 query view as input, however, we use 3 instances of the problem with different scale factors. The first one uses the original DWH dataset. The second one duplicates the original (X2), and the third one quadruplicates it (X4). Note that we are increasing the complexity exponentially, not linearly, as we are multiplying the number of tables to check for joinability, as well as materialization, and the total number of candidate views to materialize. The exact numbers that indicate the amount of work are shown in table 3.

We measure the time the system takes to perform each of these operations, and compute the ‘Normalized Time’ as the time per operation, i.e., instead of the aggregated one. A scalable system would show that the time per operation remains constant regardless the scale factor.

Figure 7: Scalability of main time-consuming procedures of DoD as the scale factor increases: two times (X2), and four times (X4).

Fig. 7 shows the results of the experiment. The X axis shows the scale factor and the Y shows the normalized time (time per operation). The figure shows that the time for Does Materialize? and Materialize remains constant despite the work growth due to the increase of the scale factor. The Does Join? operation cost increases a bit for the 4X case, but it remains within the same scale factor.

5.4.2 Ablation test

In this last section, we conduct experiments to understand the benefits of the optimizations of the ad-hoc query engine. For that, we select two query views, DWH4 and DWH2. We select these two because they perform different amount of work and produce different number of views. We execute the query views with different optimizations enabled and measure their runtime.

Fig. 8 shows the results of executing the queries without any optimization enabled, None in the X axis. We then activate optimizations incrementally. First, we activate the caching mechanism for the Does Join? operation (+C1 in the Figure), then the Does Materialize? optimization, +C2, and last we execute the full DoD, labeled All, which includes the relation cache to avoid reads from disk.

Figure 8: Performance improves from left to right as more optimizations are enabled (2 different queries)

The figure clearly shows the performance benefits of +C1 and +C2. The advantages of the optimization for the Materialize operation, which are the difference between the right-most point in the figure and the previous one, are less prominent. The relative value of each of these optimizations depends on the amount of work assigned to each of these operations during the view search. However, in aggregate, the optimizations implemented reduce the time with respect to a baseline implementation a factor of 4-6X.

Note on sampling join. Without sampling join, many of the joins on large tables run out of memory. Those that do not run out of memory take 6-8X more time to complete.

5.5 Summary of Results

The evaluation focused on two key metrics of interest. First, we showed how 4C reduces the size of the view choice space by an order of magnitude. It achieves this reduction while executing within a few minutes at most. End-to-end, DoD finds candidate views within minutes, and, in some cases within a few seconds. In essence, DoD trades expensive human time for cheaper compute cycles to accelerate view search and presentation.

6 Related Work

In this section, we explain how DoD fits into the extensive literature on data integration.

View-by-Example. The initial ideas of this approach were presented in [schemamappingasquerydiscovery], in a theoretical way, and later implemented in Clio [clio]. More recently, on the theoretical side, new contributions have helped understand the problem in more depth [approxmappings]. The current class of view-by-example systems [samples4, sfour, mweaver, exemplarqueries] aim to find views for users using a view definition, like DoD. Unlike DoD, these systems make two key assumptions that sets them apart: i) assume knowledge about how to join any pair of tables; ii) assume the total views that satisfy the input definition is small. Although the more advanced systems have a way of ranking the resulting views, such as in S4 [sfour], this does not help disambiguate semantically different views. In contrast, DoD does not make these assumptions and deals with these problems directly.

Data Integration. In the early years, data integration required humans to provide mappings (more in general, a mediated schema) between heterogeneous sources in order to understand how to combine them [clio, teenageyears, manifold]. As the number of sources has grown and become more heterogeneous, these intensive human-driven approaches have led to more automatic alternatives. DoD does not require users to provide any mediated schema or mapping between sources. Instead, users only participate at the end to select a view among a reduced choice space. Furthermore, with the multi-row presentation strategy, human involvement is further reduced.

Aiding Mapping Selection. Because creating mappings for integration is hard, many approaches appeared to assist users with the selection of the best mapping. In [datadrivenunderstanding], this problem receives a theoretical treatment. Later, practical approaches to drive the mapping selection have appeared [bonifati]. In these approaches, the mapping selection is driven by the user, who is still in charge of determining what mappings are correct. These approaches are appropriate when users are assumed to be familiar with the schema. DoD does not assume knowledge of the schema from users, and does not ask them to reason about mappings. With DoD, users can still inspect the mappings and make decisions at that level, but they also have the actual view created, which they can use to make the final decision.

Data Discovery. Data discovery solutions such as Goods [goods] and Infogather [infogather] assist with identifying relations of interest, but they do not solve the end to end problem of view creation. Data catalog solutions [catalog1, catalog2, catalog3] help organizing, annotating, and sharing data sources but do not directly help with finding or joining the datasets. Aurum [aurum] exposes a data discovery API that permits answering many different queries, as long as analysts know how to write code, which is often not the case.

Keyword search in databases. Keyword search systems [discover, banks, keyword1] are precursors of many discovery and view-by-example systems today. These systems aim to identify tuples in databases that contain the input keywords. To find such tuples, the systems identify ways to join the involved tables together. However, these systems do not take attribute names as input, they assume the existence of a PKFK-graph, and they do not help with assembling the full view, nor presenting the many views in a concise way.

7 Conclusion

DoD is a system that identifies views as combinations of multiple tables that may span multiple heterogeneous data sources. DoD only requires a query view as input, and does not assume the existence of a PKFK-graph. With the query view, it finds all views that fully or partially satisfy it, and then reduces the size of the choice space using the 4c-chasing algorithm and a presentation strategy. The 4C method is an effective way of dealing with views that are semantically different, as well as with data quality problems.

References