
Data Cleaning by Venkatesh Ganti
Data warehouses consolidate various activities of a business and often form the backbone for generating reports that support important business decisions. Errors in data tend to creep in for a variety of reasons. Some of these reasons include errors during input data collection and errors while merging data collected independently across different databases. These errors in data warehouses often result in erroneous upstream reports, and could impact business decisions negatively. Therefore, one of the critical challenges while maintaining large data warehouses is that of ensuring the quality of data in the data warehouse remains high. The process of maintaining high data quality is commonly referred to as data cleaning. In this book, we first discuss the goals of data cleaning. Often, the goals of data cleaning are not well defined and could mean different solutions in different scenarios. Toward clarifying these goals, we abstract out a common set of data cleaning tasks that often need to be addressed. This abstraction allows us to develop solutions for these common data cleaning tasks. We then discuss a few popular approaches for developing such solutions. In particular, we focus on an operator-centric approach for developing a data cleaning platform. The operator-centric approach involves the development of customizable operators that could be used as building blocks for developing common solutions. This is similar to the approach of relational algebra for query processing. The basic set of operators can be put together to build complex queries. Finally, we discuss the development of custom scripts which leverage the basic data cleaning operators along with relational operators to implement effective solutions for data cleaning tasks.-
Datalog and Logic Databases
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Blockchain-Enabled Large-Scale Transaction Management
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Query Processing over Incomplete Databases
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On Uncertain Graphs
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Big Data Integration
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An Introduction to Duplicate Detection
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Full-Text (Substring) Indexes in External Memory
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Data-Intensive Workflow Management
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Database Replication
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Data Protection from Insider Threats
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Transaction Processing on Modern Hardware
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Multidimensional Databases and Data Warehousing
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Scalable Processing of Spatial-Keyword Queries
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Generating Plans from Proofs
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Query Processing over Uncertain Databases
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Probabilistic Ranking Techniques in Relational Databases
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Relational and XML Data Exchange
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The Four Generations of Entity Resolution
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Advanced Metasearch Engine Technology
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Privacy-Preserving Data Publishing
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Data Profiling
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Non-Volatile Memory Database Management Systems
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Skylines and Other Dominance-Based Queries
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Peer-to-Peer Data Management
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Web Page Recommendation Models
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Cloud-Based RDF Data Management
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User-Centered Data Management
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Uncertain Schema Matching
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Data Exploration Using Example-Based Methods
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Querying Graphs
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Access Control in Data Management Systems
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Keyword Search in Databases
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Community Search over Big Graphs
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Human Interaction with Graphs
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Data Management in Machine Learning Systems
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Natural Language Data Management and Interfaces
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Blockchains
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Fault-Tolerant Distributed Transactions on Blockchain
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Similarity Joins in Relational Database Systems
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Query Answer Authentication
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Semantics Empowered Web 3.0
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Foundations of Data Quality Management
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Business Processes
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Information and Influence Propagation in Social Networks
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Incomplete Data and Data Dependencies in Relational Databases
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Deep Web Query Interface Understanding and Integration
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Probabilistic Databases
Venky Ganti is the co-founder and CTO of Alation Inc, where he is developing technology to effectively search, understand, and analyze structured and semi-structured data. Prior to Alation, he was a member of the Google Adwords engineering team for a few years. He helped develop the Dynamic Search Ads (DSA) product, whose goal is to completely automate the configuration and maintenance of AdWords campaigns based on an advertiser’s website and a few configuration parameters. e main technical challenge is to mine for appropriate keywords and automatically create high quality ads which match the accuracy and quality of manually configured campaigns. Prior to Google, Venky was a senior researcher at Microsoft Research (MSR). While at MSR, he worked extensively on data cleaning and integration technologies. Some of the technologies he helped develop in this context are now part of Microsoft SQL Server Integration Services, the ETL platform of Microsoft SQL Server. He also worked on leveraging rich structured databases on products, movies, people, etc., to enrich user experience for web search. Some of the tech nologies he helped develop are now part of the Bing product search. He has a Ph.D. in database systems and data mining from the University of Wisconsin-Madison. Anish Das Sarma is currently a Senior Research Scientist at Google (since May 2010), before which he was a Research Scientist at Yahoo (August 2009–April 2010). Prior to joining Yahoo research, Anish did his Ph.D. in Computer Science at Stanford University, advised by Prof. Jen nifer Widom. Anish received a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology (IIT) Bombay in 2004, and an M.S. in Computer Science from Stan ford University in 2006. Anish is a recipient of the Microsoft Graduate Fellowship, a Stanford University School of Engineering fellowship, and the IIT-Bombay Dr. Shankar Dayal Sharma Gold Medal. Anish has written over 40 technical papers, filed over 10 patents, is associate edi tor of Sigmod Record, has served on the thesis committee of a Stanford Ph.D. student, and has served on numerous program committees. Two SIGMOD and one VLDB paper co-authored by Anish were selected among the best papers of the conference, with invitations to journals. While at Stanford, Anish co-founded Shout Velocity, a social tweet ranking system that was named a top-50 fbFund Finalist for most promising upcoming start-up ideas
| SKU | Unavailable |
| ISBN 13 | 9783031007699 |
| ISBN 10 | 3031007697 |
| Title | Data Cleaning |
| Author | Venkatesh Ganti |
| Series | Synthesis Lectures On Data Management |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2013-10-01 |
| Number of pages | 69 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |














































