Big Data Integration by Xin Luna Dong, Divesh Srivastava

By Xin Luna Dong, Divesh Srivastava

The massive information period is upon us: info are being generated, analyzed, and used at an exceptional scale, and data-driven choice making is sweeping via all elements of society. because the price of knowledge explodes while it may be associated and fused with different info, addressing the large facts integration (BDI) problem is necessary to figuring out the promise of massive facts. BDI differs from conventional information integration alongside the size of quantity, speed, kind, and veracity. First, not just can information resources include a massive quantity of information, but additionally the variety of information assets is now within the thousands. moment, as a result of expense at which newly gathered info are made to be had, a few of the facts resources are very dynamic, and the variety of information assets is additionally speedily exploding. 3rd, info assets are tremendous heterogeneous of their constitution and content material, displaying huge sort even for considerably comparable entities. Fourth, the information resources are of largely differing characteristics, with major transformations within the assurance, accuracy and timeliness of information supplied. This booklet explores the growth that has been made through the knowledge integration neighborhood at the subject matters of schema alignment, checklist linkage and information fusion in addressing those novel demanding situations confronted through immense info integration. each one of those issues is roofed in a scientific manner: first beginning with a brief journey of the subject within the context of conventional info integration, by means of an in depth, example-driven exposition of contemporary cutting edge suggestions which have been proposed to deal with the BDI demanding situations of quantity, pace, type, and veracity. ultimately, it provides merging subject matters and possibilities which are particular to BDI, making a choice on promising instructions for the knowledge integration neighborhood.

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2. However, for a less available attribute such as home page URL, the situation is quite different: one needs at least 10,000 sources to cover 95% of all restaurant home page URLs. Third, they investigate the redundancy of available information using k-coverage (the fraction of entities in the database that are present in at least k different sources) to enable a higher confidence in the extracted information. 2. Fourth, they demonstrate (using user-generated restaurant reviews) that there is significant value in extracting information from the sources in the long tail.

This work is motivated by the fact that the surface web is typically modeled as a hyperlinked collection of unstructured documents, which tends to ignore the relational data contained in web documents. 5: High-quality table on the web. 23 24 1. MOTIVATION: CHALLENGES AND OPPORTUNITIES FOR BDI valuable information on just about every topic. By explicitly recognizing relational tables on the surface web, which are accessible to crawlers, web search engines can return such tables as well in response to user keyword queries.

Show that these inconsistencies cannot be effectively addressed by using naive voting, which often has an even lower accuracy than the highest accuracy from a single source. Similarly, they observe that the accuracy of deep web sources can vary a lot. 9. 9, implying that one cannot rely on a single authoritative source and ignore all other sources. 7. 9. Finally, Li et al. [2012] observe copying between deep web sources in each domain. In some cases, the copying is claimed explicitly, while in other cases it is detected by observing embedded interfaces or query redirection.

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