There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

XML employs a tree-structured data model, and, naturally, XML queries specify patterns of selection predicates on multiple elements related by a tree structure. Finding all occurrences of such a twig pattern in an XML database is a core operation for XML query processing. Prior work has typically decomposed the twig pattern into binary structural (parent-child and ancestor-descendant) relationships, and twig matching is achieved by: (i) using structural join algorithms to match the binary relationships against the XML database, and (ii) stitching together these basic matches. A limitation of this approach for matching twig patterns is that intermediate result sizes can get large, even when the input and output sizes are more manageable.In this paper, we propose a novel holistic twig join algorithm, TwigStack, for matching an XML query twig pattern. Our technique uses a chain of linked stacks to compactly represent partial results to root-to-leaf query paths, which are then composed to obtain matches for the twig pattern. When the twig pattern uses only ancestor-descendant relationships between elements, TwigStack is I/O and CPU optimal among all sequential algorithms that read the entire input: it is linear in the sum of sizes of the input lists and the final result list, but independent of the sizes of intermediate results. We then show how to use (a modification of) B-trees, along with TwigStack, to match query twig patterns in sub-linear time. Finally, we complement our analysis with experimental results on a range of real and synthetic data, and query twig patterns.

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Holistic twig joins: optimal XML pattern matching

XML queries typically specify patterns of selection predicates on multiple elements that have some specified tree structured relationships. The primitive tree structured relationships are parent-child and ancestor-descendant, and finding all occurrences of these relationships in an XML database is a core operation for XML query processing. We develop two families of structural join algorithms for this task: tree-merge and stack-tree. The tree-merge algorithms are a natural extension of traditional merge joins and the multi-predicate merge joins, while the stack-tree algorithms have no counterpart in traditional relational join processing. We present experimental results on a range of data and queries using the TIMBER native XML query engine built on top of SHORE. We show that while, in some cases, tree-merge algorithms can have performance comparable to stack-tree algorithms, in many cases they are considerably worse. This behavior is explained by analytical results that demonstrate that, on sorted inputs, the stack-tree algorithms have worst-case I/O and CPU complexities linear in the sum of the sizes of inputs and output, while the tree-merge algorithms do not have the same guarantee.

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Structural joins: a primitive for efficient XML query pattern matching

Answering Queries Using Views.

We propose a semantic model for client-side caching and replacement in a client-server database system and compare this approach to page caching and tuple caching strategies. Our caching model is based on, and derives its advantages from, three key ideas. First, the client maintains a semantic description of the data in its cache,which allows for a compact specification, as a remainder query, of the tuples needed to answer a query that are not available in the cache. Second, usage information for replacement policies is maintained in an adaptive fashion for semantic regions, which are associated with collections of tuples. This avoids the high overheads of tuple caching and, unlike page caching, is insensitive to bad clustering. Third, maintaining a semantic description of cached data enables the use of sophisticated value functions that incorporate semantic notions of locality, not just LRU or MRU, for cache replacement. We validate these ideas with a detailed performance study that includes traditional workloads as well as a workload motivated by a mobile navigation application.

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Semantic Data Caching and Replacement

String data is ubiquitous, and its management has taken on particular importance in the past few years. Approximate queries are very important on string data especially for more complex queries involving joins. This is due, for example, to the prevalence of typographical errors in data, and multiple conventions for recording attributes such as name and address. Commercial databases do not support approximate string joins directly, and it is a challenge to implement this functionality efficiently with user-defined functions (UDFs). In this paper, we develop a technique for building approximate string join capabilities on top of commercial databases by exploiting facilities already available in them. At the core, our technique relies on matching short substrings of length , called -grams, and taking into account both positions of individual matches and the total number of such matches. Our approach applies to both approximate full string matching and approximate substring matching, with a variety of possible edit distance functions. The approximate string match predicate, with a suitable edit distance threshold, can be mapped into a vanilla relational expression and optimized by conventional relational optimizers. We demonstrate experimentally the benefits of our technique over the direct use of UDFs, using commercial database systems and real data. To study the I/O and CPU behavior of approximate string join algorithms with variations in edit distance and -gram length, we also describe detailed experiments based on a prototype implementation.

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Divesh Srivastava

Papers

Holistic twig joins: optimal XML pattern matching

Structural joins: a primitive for efficient XML query pattern matching

Answering Queries Using Views.

Semantic Data Caching and Replacement

Approximate String Joins in a Database (Almost) for Free