Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.

Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.

Availability: http://maq.sourceforge.net 

Contact: [email protected]

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Fast and accurate short read alignment with Burrows–Wheeler transform

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: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.

/pdf/the-weka-data-mining-software-an-update-3zs9onav7o.pdf

The WEKA data mining software: an update

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Finding repetitive structures in genomes is important to understand their biological functions. Many modern genomic sequence data compressors also highly rely on finding the repeats over the sequences. The notion of maximal repeats captures all the repeats in a space-efficient way. Prior works on maximal repeat finding used either a suffix tree or a suffix array along with other auxiliary data structures. Their space usage is 19–50 times as large as the text size with the best engineering efforts, prohibiting their usability on massive data such as the whole human genome. Our technique is based on the Burrows-Wheeler Transform and wavelet trees. For genomic sequences stored using one byte per base, the space usage of our method is less than double of the sequence size. Our space-efficient method keeps the timing performance fast. In fact, our method is orders of magnitude faster than the prior methods for processing massive texts such as the whole human genome, since the prior methods must use external memory. For the first time, our method enables a normal computer with 8GB internal memory (actual internal memory usage is less than 6GB) to find all the maximal repeats in the whole human genome in less than 17 hours.

Time- and space-efficient maximal repeat finding using the burrows-wheeler transform and wavelet trees

This paper analyzes the coalesced hashing method, in which a portion of memory (called the address region) serves as the range of the hash function while the rest of memory (called the cellar) is devoted solely to storing records that collide when inserted. If the cellar should get full, subsequent colliders must be stored in empty slots in the address region and, thus, may cause later collisions. Varying the relative size of the cellar affects search performance. The main result of this paper expresses the average search times as a function of the number of records and the cellar size, solving the long-standing open problem described in [Knu73, §6.4-43]. We use these formulas to pick the cellar size that leads to optimum search performance and then show that this "tuned" method is competitive with several well-known hashing schemes.

Tuning the coalesced hashing method to obtain optimum performance

http://ieeexplore.ieee.org/iel5/12/5009374/05009378.pdf

Introduction --Sorting

This thesis investigates the problem of searching multimedia data. In particular, we identify poor retrieval quality and usability as two of the main hurdles for adoption of multimedia search technology. We therefore try to address the above issues by introducing three different multimedia query models and proposing the necessary infrastructure for supporting them. Specifically, we propose methods for region-based, concept-based, and constraint-based retrieval of images. 
In order to improve retrieval effectiveness, we first propose a similarity model that compares images at the granularity of image objects rather than entire images. We design an efficient algorithm to support the above similarity model and to be robust with respect to scaling and translation of image objects. We also introduce an intermediate layer on top of the query engine that automatically translates simple text queries into complex content-based queries. This concept-based query interface makes the content-based query transparent to the user and improves the system's ease of use. Finally, we consider the problem of matching arbitrary relationships among multimedia objects (e.g., spatial, temporal, and scale constraints). 
In the second part of the thesis, we study the infrastructure needed for supporting the above query models. More precisely, we consider the problem of efficiently answering top-k nearest neighbor queries. We propose SQL query language extensions and query optimization strategies for search based upon aggregate predicates, which is a generalization of top- k queries. We also investigate top-k queries over joins of ranked inputs. This ordered join problem is fundamental to multimedia query systems but also appears in many traditional database applications. In particular, we propose several algorithms, both exact and approximate, for the ordered join problem, we prove optimality results for them, and we study their behavior empirically.

Jeffrey Scott Vitter

Papers

Time- and space-efficient maximal repeat finding using the burrows-wheeler transform and wavelet trees

Tuning the coalesced hashing method to obtain optimum performance

Introduction --Sorting

Multimedia retrieval by regions, concepts, and constraints

Shortest Paths in Euclidean Graphs (Extended Abstract)