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Many applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins. In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage. Table of Contents: Introduction / Data Stream Management Systems / Streaming Data Warehouses / Conclusions.

Computer networks. --- Data structures (Computer science). --- Information theory. --- Computer Communication Networks. --- Data Structures and Information Theory. --- Data structures (Computer science)

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Data structures (Computer science) --- Database management.

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Database replication is widely used for fault-tolerance, scalability and performance. The failure of one database replica does not stop the system from working as available replicas can take over the tasks of the failed replica. Scalability can be achieved by distributing the load across all replicas, and adding new replicas should the load increase. Finally, database replication can provide fast local access, even if clients are geographically distributed clients, if data copies are located close to clients. Despite its advantages, replication is not a straightforward technique to apply, and there are many hurdles to overcome. At the forefront is replica control: assuring that data copies remain consistent when updates occur. There exist many alternatives in regard to where updates can occur and when changes are propagated to data copies, how changes are applied, where the replication tool is located, etc. A particular challenge is to combine replica control with transaction management as it requires several operations to be treated as a single logical unit, and it provides atomicity, consistency, isolation and durability across the replicated system. The book provides a categorization of replica control mechanisms, presents several replica and concurrency control mechanisms in detail, and discusses many of the issues that arise when such solutions need to be implemented within or on top of relational database systems. Furthermore, the book presents the tasks that are needed to build a fault-tolerant replication solution, provides an overview of load-balancing strategies that allow load to be equally distributed across all replicas, and introduces the concept of self-provisioning that allows the replicated system to dynamically decide on the number of replicas that are needed to handle the current load. As performance evaluation is a crucial aspect when developing a replication tool, the book presents an analytical model of the scalability potential of various replication solution. For readers that are only interested in getting a good overview of the challenges of database replication and the general mechanisms of how to implement replication solutions, we recommend to read Chapters 1 to 4. For readers that want to get a more complete picture and a discussion of advanced issues, we further recommend the Chapters 5, 8, 9 and 10. Finally, Chapters 6 and 7 are of interest for those who want get familiar with thorough algorithm design and correctness reasoning. Table of Contents: Overview / 1-Copy-Equivalence and Consistency / Basic Protocols / Replication Architecture / The Scalability of Replication / Eager Replication and 1-Copy-Serializability / 1-Copy-Snapshot Isolation / Lazy Replication / Self-Configuration and Elasticity / Other Aspects of Replication.

Computer networks. --- Data structures (Computer science). --- Information theory. --- Computer Communication Networks. --- Data Structures and Information Theory.

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Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions.

Computer networks. --- Data structures (Computer science). --- Information theory. --- Computer Communication Networks. --- Data Structures and Information Theory.

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This lecture covers several core issues in user-centered data management, including how to design usable interfaces that suitably support database tasks, and relevant approaches to visual querying, information visualization, and visual data mining. Novel interaction paradigms, e.g., mobile and interfaces that go beyond the visual dimension, are also discussed. Table of Contents: Why User-Centered / The Early Days: Visual Query Systems / Beyond Querying / More Advanced Applications / Non-Visual Interfaces / Conclusions.

Computer networks. --- Data structures (Computer science). --- Information theory. --- Computer Communication Networks. --- Data Structures and Information Theory.