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Optimization from Human Genes to Cutting Edge Technologies The challenges faced by industry today are so complex that they can only be solved through the help and participation of optimization ex perts. For example, many industries in e-commerce, finance, medicine, and engineering, face several computational challenges due to the mas sive data sets that arise in their applications. Some of the challenges include, extended memory algorithms and data structures, new program ming environments, software systems, cryptographic protocols, storage devices, data compression, mathematical and statistical methods for knowledge mining, and information visualization. With advances in computer and inform...
This book constitutes the refereed proceedings of the 19th British National Conference on Databases, BNCOD 2002, held in Sheffield, UK, in July 2002. The 10 revised full papers and 9 posters presented together with one invited paper were carefully reviewed and selected for inclusion in the volume. The book offers topical sections on query processing, data warehousing and data mining, quality and integrity, and web and distributed databases.
This volume contains the proceedings of the second joint PAPM-PROBMIV Workshop, held at the University of Copenhagen, Denmark, July 25–26, 2002 as part of the Federated Logic Conference (FLoC 2002). The PAPM-PROBMIV workshop results from the combination of two wo- shops: PAPM (Process Algebras and Performance Modeling) and PROBMIV (Probabilistic Methods in Veri?cation). The aim of the joint workshop is to bring together the researchers working across the whole spectrum of techniques for the modeling, speci?cation, analysis, and veri?cation of probabilistic systems. Probability is widely used in the design and analysis of software and hardware systems, as a means to derive e?cient algorithm...
This book presents the refereed proceedings of the 6th International Symposium on Algorithms and Computation, ISAAC '95, held in Cairns, Australia, in December 1995. The 45 revised full papers presented together with the abstracts of three invited talks were selected from a total of 130 submissions. The papers address many current aspects of research and advanced applications of algorithms and computations; among the topics covered are graph theory and graph algorithms, computational geometry, computational logics, searching and sorting, approximation and optimization, algebraic manipulation, and coding.
This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences. Throughout this handbook, the focus is kept specifically on business and consumer-oriented applications with interesting sections ranging from clustering and network analysis, meta-analytics, memetic algorithms, machine learning, recommender systems methodologies, parallel pattern mining and data mining to specific applications in market segmentation, travel, fashion or entertainment analytics. A must-read for anyone in data-analytics, marketing, behavior modelling and computational social science, interested in the lates...
Safety and Risk Modeling presents the latest theories and methods of safety and risk with an emphasis on safety and risk in modeling. It covers applications in several areas including transportations and security risk assessments, as well as applications related to current topics in safety and risk. Safety and Risk Modeling is a valuable resource for understanding the latest developments in both qualitative and quantitative methods of safety and risk analysis and their applications in operating environments. Each chapter has been written by active researchers or experienced practitioners to bridge the gap between theory and practice and to trigger new research challenges in safety and risk. Topics include: safety engineering, system maintenance, safety in design, failure analysis, and risk concept and modelling. Postgraduate students, researchers, and practitioners in many fields of engineering, operations research, management, and statistics will find Safety and Risk Modeling a state-of-the-art survey of reliability and quality in design and practice.
Graph partitioning and graph clustering are ubiquitous subtasks in many applications where graphs play an important role. Generally speaking, both techniques aim at the identification of vertex subsets with many internal and few external edges. To name only a few, problems addressed by graph partitioning and graph clustering algorithms are: What are the communities within an (online) social network? How do I speed up a numerical simulation by mapping it efficiently onto a parallel computer? How must components be organized on a computer chip such that they can communicate efficiently with each other? What are the segments of a digital image? Which functions are certain genes (most likely) responsible for? The 10th DIMACS Implementation Challenge Workshop was devoted to determining realistic performance of algorithms where worst case analysis is overly pessimistic and probabilistic models are too unrealistic. Articles in the volume describe and analyze various experimental data with the goal of getting insight into realistic algorithm performance in situations where analysis fails.
Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens. This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the...
Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes challenging. Thus, being able to perform exploratory analyses in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or the analyst, circumvents query languages by using examples as input. An example is a representative o...