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Data Integration for the Relational Web Katsarakis Michalis

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Data Integration for the Relational Web Katsarakis Michalis Presentation of the paper: Michael J. Cafarella, Alon Halevy, and Nodira Khoussainova Data integration for the relational web. Proc. VLDB Endow. 2, 1 (August 2009), for the needs of the course hy562hy562

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Octopus system in one slide

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NameInstitute Country

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Octopus system in one slide 1.Search 1.Find relations relevant to user’s query string 2.Cluster similar tables together 2.Context – Enrich relations with data from the surrounding text 3.Extend – Adorn an existing relation with additional data columns derived from other relations

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Index 1.Integration Operators 2.Algorithms 3.Implementation at Scale 4.Experiments 5.Related Work 6.Conclusions

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INTEGRATION OPERATORS 1.Integration Operators 2.Algorithms 3.Implementation at Scale 4.Experiments 5.Related Work 6.Conclusions

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Extracted Set of Relations Search Operator Relevance Ranking Clustering Keyword query string Ordered List of relevant Relations Ordered List of Clusters of Relations

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Search Operator (2) Search operator finds relevant data over the Web and then clusters the result. – Each member table of the cluster is a concrete table that contributes to the Clusters Schema Relation

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Context Operator Context Extracted Relation TT’s source web pageT enriched with new columns

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Context Operator (2) Course id Semester

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Context Operator (3) Data values that hold for every tuple are generally “projected out” and added to the Web page’s surrounding text. Context takes as input a single extracted Table T and modifies it to contain additional columns, using data retrieved from T’s source Web Page

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Extend Operator Extend Topic Keyword k Column c of relation T Extended T’

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Extend Operator (2) Enables the user to add more columns to the table by performing a join. Takes a column “c” of table T as input and a topic keyword “k”. It returns 1or more columns whose values are described by k. The new column added to T does not necessarily come from a single data source. It gathers data from large number of sources. It can also gather data from table with different label from k or no label at all.

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ALGORITHMS 1.Integration Operators 2.Algorithms 3.Implementation at Scale 4.Experiments 5.Related Work 6.Conclusions

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Algorithms Search – Ranking – Clustering Context Extend Search: – Rank the Table by relevance to Users Query – Cluster other related tables around top ranking Search result.

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Ranking Algorithms Simple Rank – Transmits the users search query to Web Search engine, obtains the URL ordering and presents the data according to that order. – Drawbacks: Ranks Individual whole page and not the data on that page. – Eg: persons home page contains a HTML list that serve as navigation list to other pages. When multiple data sets are present on the web page, SR algorithm relies on in-page ordering. (ie. In the order of its appearance) Any metadata about the HTML lists exists only in the surrounding text and not the table itself. – Cannot count hits between the query and a specific tables metadata.

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Ranking Algorithms (2) SCPRank

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Ranking Algorithms (3) SCPRank correlation between cell in extracted database and query term – Uses symmetric conditional probability to measure correlation between cell in extracted database and query term. It is defined as: How likely the term q and c appear together in a document. – SCPRank scores the table and not the cell. – It sends the query to the Search Engine, extracting a candidate set of tables. – Then it computes per-column scores, each of which is sum of per-cell SCP score in the column. – The tables overall score is the max of all of its per-column scores. – Finally it sorts the tables in the order of their scores and returns a ranked list. – Time consuming. – Compute score for first ‘r’ rows of every candidate table. – Approximating SCP score on a small subset of Web corpus.

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Embedded Appendix: symmetric conditional probability

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Ranking Algorithms (4)

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Clustering Algorithms TextCluster – computes tf-idf cosine dist between texts of table a and text of table b. SizeCluster – computes column to column similarity score that measures the difference in mean string length between them. – The overall table-to-able similarity score for a pair of table is sum of per column score for best column-to-column matching. ColumnCluster – Its similar to Size Cluster however it computes a tf-idf cosine distance using only the text found in the 2 columns.

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Embedded Appendix: tf-idf term frequency–inverse document frequency reflects how important a word is to a document in a collection or corpus – highest when the term occurs many times within a small number of documents – lower when the term occurs fewer times in a document, or occurs in many documents – lowest when the term occurs in virtually all documents

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Context Algorithms SignificantTerms – Examines the source page of the extracted table and returns the k terms with the highest tf-idf values and do not appear in the extracted data. RVP (Related View Partners) – Looks beyond the source page. – Operating on the table T, it obtains a large number of candidate related view tables, by using each value in T as parameter for a new Web Search – Then filters out tables that are unrelated to t’s source page, by removing all tables that do not contain at least one value from ST(T) – It obtains all the data value in the remaining table and ranks them according to the frequency of occurrence, returns the k highest ranked values.

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Context Algorithms (2) Hybrid – It uses the fact that the above 2 algorithm are complimentary in nature. – ST finds the context terms that RVP misses and RVP discovers the context terms that ST misses. – Hybrid returns the context term that appear in result of either algorithm.

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Extend Algorithms JoinTest Jaccardian Distance TableDistance Candidate 1α Candidate 2β Threshold: Distance ≤ Ordered List of Joinable Tables

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Extend Algorithms (2) JoinTest – Combines web search and key-matching to perform schema matching – Uses Jaccardian distance to measure the compatibility between the values of T’s column c and each column of in each candidate table. – If the distance is greater than a constant threshold t, we consider the tables to be joinable – All tables that pass this threshold, are sorted by relevance to keyword k

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Embedded Appendix: Jaccardian Distance

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Extend Algorithms (3) MultiJoin Topic Keyword k Clustering Web Search for every pair (c.cell, k) Ordered List of relevant Relations Clusters of Relations, Ordered by Relevance and JoinScore

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Extend Algorithms (4) MultiJoin – Attempts to join each tuple of in the source table T with a potentially different table Can handle the case when there is no single joinable table. – Issues a distinct web search query for every (c.cell,k) pair – Clusters the results – Ranks the clusters, using a combination of relevance score for the ranked table and a join score for the cluster. JoinScore counts how many unique values from from T’s c column elicited tables in the cluster via the web search step

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Extend Algorithms (5)

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IMPLEMENTATION AT SCALE 1.Integration Operators 2.Algorithms 3.Implementation at Scale 4.Experiments 5.Related Work 6.Conclusions

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Implementation at Scale Question: Can Octopus ever provide low latencies for a mass audience? Challenges – Traditional relevance-based Web search chalenges – Non-adjacent SCP computations for Search ScpRank algorithm – Multi-Query web searches for Context RVP algorithm Extend MultiJoin algorithm Search engines can afford to spend a huge amount of resources in order to quickly process a single query, but the same is not true for one Contopus user who yields tens of thousands of queries Case 1: 2 small prototype back-end systems Case 2: Approximation techniques to make it computationally feasible

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Non-adjacent SCP computations Not feasible to precompute word-pair statistics: just for pairs of tokens, each sampled document would yield O(w 2 ) unique token combinations Miniature search engine that fits entirely in memory – 100GiB RAM over 100 machines – Few billion web pages – No absolute precision for hitcount numbers (in order to save memory by representing document setsusing Bloom Filters)

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Embedded Appendix: Bloom Filter A Bloom filter, is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set Query can return – "inside set (may be wrong)“ – "definitely not in set"

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Multi-Query web searches The naïve Context RVP algorithm implementation requires r*d Web searches – r: number of tables processed by Context – d: average number of sampled non-numeric data cells in each table d in fairly low values (e.g.30) RVP offers a real gain in quality MultiJoin has a smaller problem, as it needs 1 query per row

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EXPERIMENTS 1.Integration Operators 2.Algorithms 3.Implementation at Scale 4.Experiments 5.Related Work 6.Conclusions

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Experiements The goal is to evaluate the quality of results generated by each Octopus Oerator Collecting Queries – Collected a diverse query load from Web Users, using Amazon Mechanical Turk. Each user suggested Topic of Data Table 2 distinct URLs that provide example tables

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Experiments (2)

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Ranking Experiments Run the ranking phase of search on each of the above 52 queries, first using SimpleRank, then ScpRank Two judges, drawn from Amazon Mechanical Turk, labeled the table’s relevance to the query, on a scale 1-5. Table was marked as relevant only when both judges gave score 4 or higher

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Ranking Experiments (2) Results – ScpRank performs substantially better than SimpleRank, especially in Top-2 case. – The extra computational overhead clearly offers real gains in result quality

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Clustering Experiments Issued queries and obtained a sorted list of tables, using ScpRank – Best Table for each result manually chosen and used as center input to the clustering system Cluster quality assessed by computing the percentage of queries in which a k-sized cluster contains a table that is “highly similar” to the center. Determine whether a table is “highly similar”, by asking two users from Amazon Mechanical Turk to rate the similarity of the pair in a scale 1-5. Table was marked as “highly similar” only when both judges gave score 4 or higher

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Clustering Experiments (2) Results – k: cluster size: the system has only k “guesses” to find a table that is similar to the center – Little variance in quality across all algorithms

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Context Experiments Top-1 relevant table per query Two of the authors manually reviewed each Table’s source page, noting terms that appeared to be useful context values The values that both reviewers noted, were added in the test set of true context values Within the test set, there is a median of 3 test context values per table Measured the percentage of tables, where a true context value is included in the top-k of the context terms, generated by each algorithm

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Context Experiments (2) Results – Context can adorn a table with useful data from the surrounding text over 80% of the time – Although the RVP and SignificantTerms are not disjoint, RVP is able to discover new context terms that were missed by SignificantTerms – SignificantTerms does not yield the best output quality, but it is still efficient and very easy to implement

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Extend Experiments A small number of queries that appear to be Extend-able were chosen Top-1 ranked “relevant” table returned from search was used Join column c and topic keyword query k were chosen by hand opting for values that appear to be ammendable to Extend processing

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Extend Experiments (2) Results – JoinTest (tries to find a single satisfactory table) only found extended tuples in 3 cases Countries US Cities UK Political Parties – In this 3 cases, 60% of tuples were extended – MultiJoin found extended data for all cases – On average, 33% of the source tuples were extended – MultiJoin has a lower rate of tuple-extension than JoinTest – MultiJoin finds an average of 45.5 correct extension values for every successfully –extended source tuple. – MultiJoin shows flexibility on per-tuple approach – With MultiJoin, fewer rows may be extended, but at least some data can be found.

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Experiments Summary It is possible to obtain high-quality results for all three Octopus operators Even with imperfect outputs, Octopus improves the productivity of the user Promising areas of future research – Output quality – Algorithmic runtime performance

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RELATED WORK 1.Integration Operators 2.Algorithms 3.Implementation at Scale 4.Experiments 5.Related Work 6.Conclusions

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Related Work Data Integration on Web called as “MashUp” is increasingly popular area of work. The Yahoo Pipes allows the user to graphically describe the flow of data (structured data only) CIMPLE is data integration system for web use designed to construct community websites.

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CONCLUSIONS 1.Integration Operators 2.Algorithms 3.Implementation at Scale 4.Experiments 5.Related Work 6.Conclusions

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Conclusions OCTOPUS allows the user to integrate data from many unstructured data source. It offers access to orders of magnitude of data sources, frees the user from having to design or even know about the mediated schema.

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Questions

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