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 unheard of scale, and data-driven determination making is sweeping via all facets of society. because the price of information explodes while it may be associated and fused with different information, addressing the large info integration (BDI) problem is necessary to knowing the promise of huge info. BDI differs from conventional information integration alongside the scale of quantity, speed, sort, and veracity. First, not just can facts assets include a tremendous quantity of knowledge, but in addition the variety of facts assets is now within the thousands. moment, due to the fee at which newly amassed facts are made to be had, a few of the facts resources are very dynamic, and the variety of info resources is usually speedily exploding. 3rd, info assets are tremendous heterogeneous of their constitution and content material, displaying substantial type even for considerably comparable entities. Fourth, the knowledge resources are of largely differing traits, with major modifications within the assurance, accuracy and timeliness of information supplied. This ebook explores the growth that has been made through the information integration neighborhood at the issues of schema alignment, list linkage and information fusion in addressing those novel demanding situations confronted by way of gigantic information integration. each one of those subject matters is roofed in a scientific manner: first beginning with a short travel of the subject within the context of conventional info integration, through a close, example-driven exposition of modern leading edge strategies which were proposed to deal with the BDI demanding situations of quantity, pace, style, and veracity. ultimately, it offers merging subject matters and possibilities which are particular to BDI, selecting promising instructions for the knowledge integration neighborhood.

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3. The quality of the extracted knowledge is also evaluated against the Freebase knowledge base as a gold standard. Specifically, if an extracted triple s , p, o occurs in Freebase, it is considered to be true; if s , p, o does not occur in Freebase, but s , p, o does, then the extracted triple s , p, o is considered to be false; otherwise it is not included in the gold standard. 6: Contributions and overlaps between different types of web contents [Dong et al. 2014b]. Main Results We categorize the main results of this study according to the investigated “V” dimensions.

He et al. [2007] and Madhavan et al. [2007] experimentally study the volume, velocity, and domain-level variety of data sources available on the deep web. 1. What is the scale of the deep web? For example, how many query interfaces to databases exist on the web? How many web databases are accessible through such query interfaces? How many web sources provide query interfaces to databases? How have these deep web numbers changed over time? 2. com/answers/99725161/how-many-sea-ports-in-world (accessed on October 1, 2014).

2012]. 40% of the home pages of restaurants. 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.

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