With the ever increasing volume of data, data quality problems abound. Multiple, yet different representations of the same real-world objects in data, duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically detecting duplicates is difficult: First, duplicate representations are usually not identical but slightly differ in their values. Second, in principle all pairs of records should be compared, which is infeasible for large volumes of data. This lecture examines closely the two main components to overcome these difficulties: (i) Similarity measures are used to automatically identify duplicates when comparing two records. Well-chosen similarity measures improve the effectiveness of duplicate detection. (ii) Algorithms are developed to perform on very large volumes of data in search for duplicates. Well-designed algorithms improve the efficiency of duplicate detection. Finally, we discuss methods to evaluate the success of duplicate detection. Table of Contents: Data Cleansing: Introduction and Motivation / Problem Definition / Similarity Functions / Duplicate Detection Algorithms / Evaluating Detection Success / Conclusion and Outlook / Bibliography
Our daily lives, our culture and our politics are now shaped by the digital condition as large numbers of people involve themselves in contentious negotiations of meaning in ever more dimensions of life, from the trivial to the profound. They are making use of the capacities of complex communication infrastructures, currently dominated by social mass media such as Twitter and Facebook, on which they have come to depend. Amidst a confusing plurality, Felix Stalder argues that are three key constituents of this condition: the use of existing cultural materials for one's own production, the way in which new meaning is established as a collective endeavour, and the underlying role of algorithms and automated decision-making processes that reduce and give shape to massive volumes of data. These three characteristics define what Stalder calls 'the digital condition'. Stalder also examines the profound political implications of this new culture. We stand at a crossroads between post-democracy and the commons, a concentration of power among the few or a genuine widening of participation, with the digital condition offering the potential for starkly different outcomes. This ambitious and wide-ranging theory of our contemporary digital condition will be of great interest to students and scholars in media and communications, cultural studies, and social, political and cultural theory, as well as to a wider readership interested in the ways in which culture and politics are changing today.
This book, first published in English translation in 1947, is the fascinating autobiography of Dr. Felix Kersten, a Russian-born Finnish osteopath who tended to Heinrich Himmler in Germany during World War II and who contended he had obtained some amelioration of treatment of Jews and others.
With the ever increasing volume of data, data quality problems abound. Multiple, yet different representations of the same real-world objects in data, duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically detecting duplicates is difficult: First, duplicate representations are usually not identical but slightly differ in their values. Second, in principle all pairs of records should be compared, which is infeasible for large volumes of data. This lecture examines closely the two main components to overcome these difficulties: (i) Similarity measures are used to automatically identify duplicates when comparing two records. Well-chosen similarity measures improve the effectiveness of duplicate detection. (ii) Algorithms are developed to perform on very large volumes of data in search for duplicates. Well-designed algorithms improve the efficiency of duplicate detection. Finally, we discuss methods to evaluate the success of duplicate detection. Table of Contents: Data Cleansing: Introduction and Motivation / Problem Definition / Similarity Functions / Duplicate Detection Algorithms / Evaluating Detection Success / Conclusion and Outlook / Bibliography
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