LEADER 04101oam 2200529 450 001 9910437569503321 005 20190911112726.0 010 $a3-642-41482-6 024 7 $a10.1007/978-3-642-41482-4 035 $a(OCoLC)865010802 035 $a(MiFhGG)GVRL6UPG 035 $a(EXLCZ)993710000000024419 100 $a20131025d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aCombinatorial search $efrom algorithms to systems /$fYoussef Hamadi 205 $a1st ed. 2013. 210 1$aHeidelberg [Germany] :$cSpringer,$d2013. 215 $a1 online resource (xiii, 139 pages) $cillustrations (some color) 225 0 $aGale eBooks 300 $aDescription based upon print version of record. 311 $a3-642-41481-8 320 $aIncludes bibliographical references. 327 $aChap. 1 - Introduction -- Chap. 2 - Boosting Distributed Constraint Networks -- Chap. 3 - Parallel Tree Search for Satisfiability -- Chap. 4 - Parallel Local Search for Satisfiability -- Chap. 5 - Learning Variables Dependencies -- Chap. 6 - Continuous Search -- Chap. 7 - Autonomous Search -- Chap. 8 - Conclusion and Perspectives. 330 $aAlthough they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to efficiently explore these regions. Various mathematical formalisms may be used to express and tackle combinatorial problems, among them the constraint satisfaction problem (CSP) and the propositional satisfiability problem (SAT). These algorithms, or constraint solvers, apply search space reduction through inference techniques, use activity-based heuristics to guide exploration, diversify the searches through frequent restarts, and often learn from their mistakes. In this book the author focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver. Information can be shared between multiple constraint solvers simultaneously working on the same instance, or information can help achieve good performance while solving a large set of related instances. In the first case, information sharing has to be performed at the expense of the underlying search effort, since a solver has to stop its main effort to prepare and commu nicate the information to other solvers; on the other hand, not sharing information can incur a cost for the whole system, with solvers potentially exploring unfeasible spaces discovered by other solvers. In the second case, sharing performance measures can be done with little overhead, and the goal is to be able to tune a constraint solver in relation to the characteristics of a new instance ? this corresponds to the selection of the most suitable algorithm for solving a given instance. The book is suitable for researchers, practitioners, and graduate students working in the areas of optimization, search, constraints, and computational complexity. 606 $aCombinatorial optimization 606 $aComputer algorithms 606 $aConstraint programming (Computer science) 606 $aElectronic information resource searching 615 0$aCombinatorial optimization. 615 0$aComputer algorithms. 615 0$aConstraint programming (Computer science) 615 0$aElectronic information resource searching. 676 $a004 676 $a004.0151 676 $a006.3 676 $a519.6 700 $aHamadi$b Youssef$4aut$4http://id.loc.gov/vocabulary/relators/aut$01065558 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910437569503321 996 $aCombinatorial search$93871491 997 $aUNINA LEADER 04692nam 22005415 450 001 9910158734703321 005 20220413190538.0 024 7 $a10.1007/978-3-319-47431-1 035 $a(CKB)3710000001008933 035 $a(DE-He213)978-3-319-47431-1 035 $a(MiAaPQ)EBC4778941 035 $a(PPN)198342136 035 $a(EXLCZ)993710000001008933 100 $a20170106d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLarge-scale graph processing using Apache Giraph /$fby Sherif Sakr, Faisal Moeen Orakzai, Ibrahim Abdelaziz, Zuhair Khayyat 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XXV, 197 p. 102 illus., 87 illus. in color.) 311 $a3-319-47430-8 311 $a3-319-47431-6 327 $a1. Introduction -- 2. Getting started with Giraph -- 3. Giraph-In-Action: Implementing Popular Graph Algorithms using Giraph -- 4. Giraph Programming Optimizations: Tips and Tricks -- 5. Similar Systems to Giraph -- 6. Conclusions. 330 $aThis book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms. The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained.  Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system?s utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems. 606 $aDatabase management 606 $aBig data 606 $aData structures (Computer science) 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017 615 0$aDatabase management. 615 0$aBig data. 615 0$aData structures (Computer science) 615 14$aDatabase Management. 615 24$aBig Data/Analytics. 615 24$aData Structures. 676 $a005.74 700 $aSakr$b Sherif$4aut$4http://id.loc.gov/vocabulary/relators/aut$0866736 702 $aOrakzai$b Faisal Moeen$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aAbdelaziz$b Ibrahim$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aKhayyat$b Zuhair$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910158734703321 996 $aLarge-Scale Graph Processing Using Apache Giraph$91934710 997 $aUNINA LEADER 01068nam0 22002531i 450 001 UON00200129 005 20231205103249.817 100 $a20030730d1937 |0itac50 ba 101 $aeng 102 $aGB 105 $a|||| ||||| 200 1 $aˆThe ‰ Jews$fHilaire Belloc 210 $aLonbon$cConstable & Company Ltd.$d1937. LV$d308 p. ; 19 cm. 316 $aIl volume è collocato nei cantinati della Sezione Giusso, attualmente non accessibili.$5IT-UONSI III STORIAASI/0407 606 $aEBREI$xSTUDI$3UONC041656$2FI 620 $aGB$dLondon$3UONL003044 700 1$aBelloc$bHilaire$3UONV118759$0438772 712 $aConstable$3UONV248877$4650 801 $aIT$bSOL$c20250627$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00200129 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI ASI 0407 $eSI MR 10380 5 0407 Il volume è collocato nei cantinati della Sezione Giusso, attualmente non accessibili. 996 $aJews$91254972 997 $aUNIOR