Big Data Integration by Xin Luna Dong, Divesh Srivastava

Big Data Integration by Xin Luna Dong, Divesh Srivastava

By Xin Luna Dong, Divesh Srivastava

The large facts period is upon us: facts are being generated, analyzed, and used at an extraordinary scale, and data-driven choice making is sweeping via all points of society. because the worth of information explodes while it may be associated and fused with different info, addressing the massive facts integration (BDI) problem is necessary to understanding the promise of massive facts. BDI differs from conventional facts integration alongside the size of quantity, pace, type, and veracity. First, not just can info resources include a big quantity of information, but in addition the variety of facts resources is now within the thousands. moment, as a result of price at which newly amassed info are made to be had, a few of the information assets are very dynamic, and the variety of facts assets is usually speedily exploding. 3rd, facts resources are tremendous heterogeneous of their constitution and content material, showing significant style even for considerably related entities. Fourth, the information assets are of largely differing characteristics, with major changes within the assurance, accuracy and timeliness of information supplied. This e-book explores the growth that has been made by way of the knowledge integration neighborhood at the themes of schema alignment, list linkage and information fusion in addressing those novel demanding situations confronted via immense facts integration. every one of those subject matters is roofed in a scientific approach: first beginning with a brief journey of the subject within the context of conventional facts integration, by way of an in depth, example-driven exposition of modern leading edge recommendations which have been proposed to deal with the BDI demanding situations of quantity, speed, sort, and veracity. ultimately, it provides merging themes and possibilities which are particular to BDI, settling on promising instructions for the knowledge integration neighborhood.

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No. sites per entity Diameter No. conn. Percent entities comp. in largest comp. 3: Connectivity (between entities and sources) for the nine domains studied by Dalvi et al. [2012]. the demand for and the availability of review information reduces towards the tail, information availability reduces at a faster rate, suggesting that tail extraction can be valuable in spite of the lower demand. 3, they observe that there is a significant amount of data redundancy (tens to hundreds of sources per entity on average), and the data within a domain is well connected.

2. There are also interesting opportunities enabled by BDI and the infrastructures used for managing and analyzing big data, to effectively address these challenges. We focus on three such opportunities. 1 DATA REDUNDANCY The data obtained from different sources often overlap, resulting in a high data redundancy across the large number of sources that need to be integrated. 27 28 1. MOTIVATION: CHALLENGES AND OPPORTUNITIES FOR BDI This is evident in our motivating Flights example, where information such as Departure Airport, Scheduled Departure Time, Arrival Airport, and Scheduled Arrival Time about Airline1’s flight 49 can be obtained from each of the sources Airline1, Airport3 and Airfare4.

Schedule. 4 Outline of Book flight departure and arrival times, which can vary considerably over short time periods; airplane maintenance and repair logs provide insight about airplane quality over time, and so on. While the case studies that we presented earlier in this chapter do not specifically deal with long data, some of the techniques that we will describe in subsequent chapters, especially for record linkage (Chapter 3) and data fusion (Chapter 4), take considerable advantage of the presence of long data.

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