Dennis Shasha – författare

Tuning your database for optimal performance means more than following a few short steps in a vendor-specific guide. For maximum improvement, you need a broad and deep knowledge of basic tuning principles, the ability to gather data in a systematic way, and the skill to make your system run faster. This is an art as well as a science, and Database Tuning: Principles, Experiments, and Troubleshooting Techniques will help you develop portable skills that will allow you to tune a wide variety of database systems on a multitude of hardware and operating systems. Further, these skills, combined with the scripts provided for validating results, are exactly what you need to evaluate competing database products and to choose the right one.- Forward by Jim Gray, with invited chapters by Joe Celko and Alberto Lerner- Includes industrial contributions by Bill McKenna (RedBrick/Informix), Hany Saleeb (Oracle), Tim Shetler (TimesTen), Judy Smith (Deutsche Bank), and Ron Yorita (IBM)- Covers the entire system environment: hardware, operating system, transactions, indexes, queries, table design, and application analysis- Contains experiments (scripts available on the author''s site) to help you verify a system''s effectiveness in your own environment- Presents special topics, including data warehousing, Web support, main memory databases, specialized databases, and financial time series- Describes performance-monitoring techniques that will help you recognize and troubleshoot problems

Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.

This book presents, in one convenient volume, the work of researchers in the field of pattern discovery, a fundamental operation used in the process of extracting knowledge from biomolecular data. The significant growth of the size of biomolecular data has lead scientists to place increasing importance on developing novel techniques for this knowledge extraction. With this fact in mind, the editors have compiled this clear, up-to-date summary of the principle techniques of pattern discovery in molecular biology, in the hope that readers will build on these techniques and make discoveries of their own. The techniques are drawn from many fields of mathematical science, including graph theory, information theory, statistics, and computer vision. Because there are a variety of equally valid methods of finding patterns, the book presents methods in their purest form so that readers can choose the method or combination that best fits their applications. The contributions are organized into three parts: Finding Patterns in DNA and Protein Sequences; Finding Patterns in 3D Structures, and System Components for Discovery. This resource is essential to anyone who conducts research in biocomputing, and can serve as an excellent supplementary text for biocomputing courses.

Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.

Iraq's Last Jews is a collection of first-person accounts by Jews about their lives in Iraq's once-vibrant, 2500 year-old Jewish community and about the disappearance of that community in the middle of the 20th century. This book tells the story of this last generation of Iraqi Jews, who both reminisce about their birth country and describe the persecution that drove them out, the result of Nazi influences, growing Arab nationalism, and anger over the creation of the State of Israel.

Imagine living during the Renaissance and being able to interview that era's greatest scientists about their inspirations, discoveries, and personal interests. The latter half of our century has seen its own Renaissance--informations technology has changed irrevocable the way we live, work, and think about the world. We are fortunate, therefore, that the authors of Out of Their Minds have been able to talk so candidly with the founders of computer science....

The Fourth International Workshop on Database Programming Languages - Object Models and Languages (DBPL-4) took place in Manhattan, New York City, 30 August-1 September 1993. The areas of interest and the format of DBPL-4 focused on the integration of programming languages, object models, type systems and database systems. As in the previous DBPL workshops, the setting was informal, allowing the participants to actively discuss and argue about the ideas presented in the talks. The comments and remarks made by the participants during and after the presentations were taken into account in the preparation of the final versions of the papers. The result, we believe, is a set of excellent papers. The DBPL sequence is closely related to the sequence of International Workshops on Persistent Object Systems (POS), first started in 1985. While the DBPL workshops focus on language and model issues, the POS workshops have focused on implementation issues; thus the two sequences complement each other. Many researchers participate in both workshop series. The eight sessions of the technical program of DBPL-4 were as follows: 1. Bulk types and their query languages (two sessions). 2. Object models and languages. 3. Data types with order. 4. Mechanisms to support persistence, reflection, and extensibility. 5. Query optimization and integrity constraints. 6. Logic-based models. 7. Implementation and performance issues.

Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation.Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable.

Tuning your database for optimal performance means more than following a few short steps in a vendor-specific guide. For maximum improvement, you need a broad and deep knowledge of basic tuning principles, the ability to gather data in a systematic way, and the skill to make your system run faster. This is an art as well as a science, and Database Tuning: Principles, Experiments, and Troubleshooting Techniques will help you develop portable skills that will allow you to tune a wide variety of database systems on a multitude of hardware and operating systems. Further, these skills, combined with the scripts provided for validating results, are exactly what you need to evaluate competing database products and to choose the right one.

8. 1. 1 Protein Subcellular Location The life sciences have entered the post-genome era where the focus of biologicalresearchhasshiftedfromgenomesequencestoproteinfunctionality. Withwhole-genomedraftsofmouseandhumaninhand,scientistsareputting more and more e?ort into obtaining information about the entire proteome in a given cell type. The properties of a protein include its amino acid sequences, its expression levels under various developmental stages and in di?erenttissues,its3Dstructureandactivesites,itsfunctionalandstructural binding partners, and its subcellular location. Protein subcellular location is important for understanding protein function inside the cell. For example, the observation that the product of a gene is localized in mitochondria will support the hypothesis that this protein or gene is involved in energy metabolism. Proteins localized in the cytoskeleton are probably involved in intracellular tra?cking and support. The context of protein functionality is well represented by protein subcellular location. Proteins have various subcellular location patterns [250]. One major category of proteins is synthesized on free ribosomes in the cytoplasm. Soluble proteins remain in the cytoplasm after their synthesis and function as small factories catalyzing cellular metabolites. Other proteins that have a target signal in their sequences are directed to their target organelle (such as mitochondria) via posttranslational transport through the organelle membrane. Nuclear proteins are transferred through pores on the nuclear envelope to the nucleus and mostly function as regulators. The second major category of proteins is synthesized on endoplasmic reticulum(ER)-associated ribosomes and passes through the reticuloendothelial system, consisting of the ERand the Golgi apparatus.

8. 1. 1 Protein Subcellular Location The life sciences have entered the post-genome era where the focus of biologicalresearchhasshiftedfromgenomesequencestoproteinfunctionality. Withwhole-genomedraftsofmouseandhumaninhand,scientistsareputting more and more e?ort into obtaining information about the entire proteome in a given cell type. The properties of a protein include its amino acid sequences, its expression levels under various developmental stages and in di?erenttissues,its3Dstructureandactivesites,itsfunctionalandstructural binding partners, and its subcellular location. Protein subcellular location is important for understanding protein function inside the cell. For example, the observation that the product of a gene is localized in mitochondria will support the hypothesis that this protein or gene is involved in energy metabolism. Proteins localized in the cytoskeleton are probably involved in intracellular tra?cking and support. The context of protein functionality is well represented by protein subcellular location. Proteins have various subcellular location patterns [250]. One major category of proteins is synthesized on free ribosomes in the cytoplasm. Soluble proteins remain in the cytoplasm after their synthesis and function as small factories catalyzing cellular metabolites. Other proteins that have a target signal in their sequences are directed to their target organelle (such as mitochondria) via posttranslational transport through the organelle membrane. Nuclear proteins are transferred through pores on the nuclear envelope to the nucleus and mostly function as regulators. The second major category of proteins is synthesized on endoplasmic reticulum(ER)-associated ribosomes and passes through the reticuloendothelial system, consisting of the ERand the Golgi apparatus.

8. 1. 1 Protein Subcellular Location The life sciences have entered the post-genome era where the focus of biologicalresearchhasshiftedfromgenomesequencestoproteinfunctionality. Withwhole-genomedraftsofmouseandhumaninhand,scientistsareputting more and more e?ort into obtaining information about the entire proteome in a given cell type. The properties of a protein include its amino acid sequences, its expression levels under various developmental stages and in di?erenttissues,its3Dstructureandactivesites,itsfunctionalandstructural binding partners, and its subcellular location. Protein subcellular location is important for understanding protein function inside the cell. For example, the observation that the product of a gene is localized in mitochondria will support the hypothesis that this protein or gene is involved in energy metabolism. Proteins localized in the cytoskeleton are probably involved in intracellular tra?cking and support. The context of protein functionality is well represented by protein subcellular location. Proteins have various subcellular location patterns [250]. One major category of proteins is synthesized on free ribosomes in the cytoplasm. Soluble proteins remain in the cytoplasm after their synthesis and function as small factories catalyzing cellular metabolites. Other proteins that have a target signal in their sequences are directed to their target organelle (such as mitochondria) via posttranslational transport through the organelle membrane. Nuclear proteins are transferred through pores on the nuclear envelope to the nucleus and mostly function as regulators. The second major category of proteins is synthesized on endoplasmic reticulum(ER)-associated ribosomes and passes through the reticuloendothelial system, consisting of the ERand the Golgi apparatus.

In the history of modern computation, large mechanical calculators preceded computers. A person would sit there punching keys according to a procedure and a number would eventually appear. Once calculators became fast enough, it became obvious that the critical path was the punching rather than the calculation itself. That is what made the stored program concept vital to further progress. Once the instructions were stored in the machine, the entire computation could run at the speed of the machine. This book shows how to do the same thing for DNA computing. Rather than asking a robot or a person to pour in specific strands at different times in order to cause a DNA computation to occur (by analogy to a person punching numbers and operations into a mechanical calculator), the DNA instructions are stored within the solution and guide the entire computation. We show how to store straight line programs, conditionals, loops, and a rudimentary form of subroutines.To achieve this goal, the book proposes a complete language for describing the intrinsic topology of DNA complexes and nanomachines, along with the dynamics of such a system. We then describe dynamic behavior using a set of basic transitions, which operate on a small neighborhood within a complex in a well-defined way. These transitions can be formalized as purely syntactical functions of the string representations.Building on that foundation, the book proposes a novel machine motif which constitutes an instruction stack, allowing for the clocked release of an arbitrary sequence of DNA instruction or data strands. The clock mechanism is built of special strands of DNA called ""tick"" and ""tock."" Each time a ""tick"" and ""tock"" enter a DNA solution, a strand is released from an instruction stack (by analogy to the way in which as a clock cycle in an electronic computer causes a new instruction to enter a processing unit). As long as there remain strands on the stack, the next cycle will release a new instruction strand. Regardless of the actual strand or component to be released at any particular clock step, the ""tick"" and ""tock"" fuel strands remain the same, thus shifting the burden of work away from the end user of a machine and easing operation. Pre-loaded stacks enable the concept of a stored program to be realized as a physical DNA mechanism.A conceptual example is given of such a stack operating a walker device. The stack allows for a user to operate such a clocked walker by means of simple repetition of adding two fuel types, in contrast to the previous mechanism of adding a unique fuel -- at least 12 different types of strands -- for each step of the mechanism.We demonstrate by a series of experiments conducted in Ned Seeman's lab that it is possible to ""initialize"" a clocked stored program DNA machine. We end the book with a discussion of the design features of a programming language for clocked DNA programming. There is a lot left to do.Table of Contents:Introduction / Notation / A Topological Description of DNA Computing / Machines and Motifs / Experiment: Storing Clocked Programs in DNA / A Clocked DNA Programming Language

Search structures support the fundamental data storage primitives on key-value pairs: insert a pair, delete by key, search by key, and update the value associated with a key. Concurrent search structures are parallel algorithms to speed access to search structures on multicore and distributed servers. These sophisticated algorithms perform fine-grained synchronization between threads, making them notoriously difficult to design correctly. Indeed, bugs have been found both in actual implementations and in the designs proposed by experts in peer-reviewed publications. The rapid development and deployment of these concurrent algorithms has resulted in a rift between the algorithms that can be verified by the state-of-the-art techniques and those being developed and used today. The goal of this book is to show how to bridge this gap in order to bring the certified safety of formal verification to high-performance concurrent search structures. Similar techniques and frameworks can be applied to concurrent graph and network algorithms beyond search structures.

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea/ Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico's 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B

Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.

In the history of modern computation, large mechanical calculators preceded computers. A person would sit there punching keys according to a procedure and a number would eventually appear. Once calculators became fast enough, it became obvious that the critical path was the punching rather than the calculation itself. That is what made the stored program concept vital to further progress. Once the instructions were stored in the machine, the entire computation could run at the speed of the machine. This book shows how to do the same thing for DNA computing. Rather than asking a robot or a person to pour in specific strands at different times in order to cause a DNA computation to occur (by analogy to a person punching numbers and operations into a mechanical calculator), the DNA instructions are stored within the solution and guide the entire computation. We show how to store straight line programs, conditionals, loops, and a rudimentary form of subroutines.To achieve this goal, the book proposes a complete language for describing the intrinsic topology of DNA complexes and nanomachines, along with the dynamics of such a system. We then describe dynamic behavior using a set of basic transitions, which operate on a small neighborhood within a complex in a well-defined way. These transitions can be formalized as purely syntactical functions of the string representations.Building on that foundation, the book proposes a novel machine motif which constitutes an instruction stack, allowing for the clocked release of an arbitrary sequence of DNA instruction or data strands. The clock mechanism is built of special strands of DNA called ""tick"" and ""tock."" Each time a ""tick"" and ""tock"" enter a DNA solution, a strand is released from an instruction stack (by analogy to the way in which as a clock cycle in an electronic computer causes a new instruction to enter a processing unit). As long as there remain strands on the stack, the next cycle will release a new instruction strand. Regardless of the actual strand or component to be released at any particular clock step, the ""tick"" and ""tock"" fuel strands remain the same, thus shifting the burden of work away from the end user of a machine and easing operation. Pre-loaded stacks enable the concept of a stored program to be realized as a physical DNA mechanism.A conceptual example is given of such a stack operating a walker device. The stack allows for a user to operate such a clocked walker by means of simple repetition of adding two fuel types, in contrast to the previous mechanism of adding a unique fuel -- at least 12 different types of strands -- for each step of the mechanism.We demonstrate by a series of experiments conducted in Ned Seeman''s lab that it is possible to ""initialize"" a clocked stored program DNA machine. We end the book with a discussion of the design features of a programming language for clocked DNA programming. There is a lot left to do.Table of Contents:Introduction / Notation / A Topological Description of DNA Computing / Machines and Motifs / Experiment: Storing Clocked Programs in DNA / A Clocked DNA Programming Language

Search structures support the fundamental data storage primitives on key-value pairs: insert a pair, delete by key, search by key, and update the value associated with a key. Concurrent search structures are parallel algorithms to speed access to search structures on multicore and distributed servers. These sophisticated algorithms perform fine-grained synchronization between threads, making them notoriously difficult to design correctly. Indeed, bugs have been found both in actual implementations and in the designs proposed by experts in peer-reviewed publications. The rapid development and deployment of these concurrent algorithms has resulted in a rift between the algorithms that can be verified by the state-of-the-art techniques and those being developed and used today. The goal of this book is to show how to bridge this gap in order to bring the certified safety of formal verification to high-performance concurrent search structures. Similar techniques and frameworks can be applied to concurrent graph and network algorithms beyond search structures.

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then systematically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. The ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea /Bias Corrected Confidence Intervals / Pragmatic Considerations When Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico''s 2004 Presidential Ballots / References

Statistics is the activity of inferring results about a population given a sample. Historically, statistics books assume an underlying distribution to the data (typically, the normal distribution) and derive results under that assumption. Unfortunately, in real life, one cannot normally be sure of the underlying distribution. For that reason, this book presents a distribution-independent approach to statistics based on a simple computational counting idea called resampling. This book explains the basic concepts of resampling, then system atically presents the standard statistical measures along with programs (in the language Python) to calculate them using resampling, and finally illustrates the use of the measures and programs in a case study. The text uses junior high school algebra and many examples to explain the concepts. Th e ideal reader has mastered at least elementary mathematics, likes to think procedurally, and is comfortable with computers. Table of Contents: The Basic Idea/ Pragmatic Considerations when Using Resampling / Terminology / The Essential Stats / Case Study: New Mexico''s 2004 Presidential Ballots / References / Bias Corrected Confidence Intervals / Appendix B

Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy, gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.