Microdata Deduplication with Spark
Name
Khalil Ur Rehman
Abstract
The web is transforming from traditional web to web of data, where information is presented in such a way that it is readable by machines as well as human. As a part of this transformation, every day more and more websites implant structured data, e.g. product, person, organization, place etc., into the HTML pages. To implant the structured data different encoding vocabularies, such as RDFa, microdata, and microformats, are used. Microdata is the most recent addition to these structure data embedding standards, but it has gained more popularity over other formats in less time. Similarly, progress has been made in the extraction of the structured data from web pages, which has resulted in open source tools such as Apache Any23 and non-profit Common Crawl project. Any23 allows extraction of microdata from the web pages with less effort, whereas Common Crawl extracts data from websites and provides it publically for download. In fact, the microdata extraction tools only take care of parsing and data transformation steps of data cleansing. Although with the help of these state-of-the-art extraction tools microdata can be easily extracted, before the extracted data used in potential applications, duplicates should be removed and data unified. Since microdata origins from arbitrary web resources, it has arbitrary quality as well and should be treated correspondingly.
The main purpose of this thesis is to develop the effective mechanism for deduplication of microdata on the web scale. Although the deduplication algorithms have reached relative maturity, however, these algorithm needs to be executed on specific datasets for fine-tuning. In particular, the need to identify the most suitable length of sorting key in sorted-based deduplication approach. The present work identifies the optimum length of the sorting key in the context of extracted product microdata deduplication. Due to large volumes of data to be processed continuously, Apache Spark will be used for implementing the necessary procedures.
The main purpose of this thesis is to develop the effective mechanism for deduplication of microdata on the web scale. Although the deduplication algorithms have reached relative maturity, however, these algorithm needs to be executed on specific datasets for fine-tuning. In particular, the need to identify the most suitable length of sorting key in sorted-based deduplication approach. The present work identifies the optimum length of the sorting key in the context of extracted product microdata deduplication. Due to large volumes of data to be processed continuously, Apache Spark will be used for implementing the necessary procedures.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Peep Küngas
Defence year
2016