managing and sharing customer data ppt
Managing Time in Relational Databases: How to Design, Update and Query Temporal Data pptx
... Taxonomy of Methods for Managing Temporal Data 34The Root Node of the Taxonomy 35Queryable Temporal Data: Events and States 37State Temporal Data: Uni-Temporal and Bi-Temporal Data 41Glossary References ... Kimball’s either/or,they took a both /and stance, and advocated the use of opera-tional data stores (ODSs), historical data warehouses and dimen-sional data marts, with each one serving different ... whoreally understands bi-temporal data management. Tom’s under-standing, writing abilities and contributions to this work arepriceless. His patience and willingness to compromise and workwith...
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Managing and Mining Graph Data part 1 pptx
... 12. Graph Management and Mining Applications 33. Summary 8References 92Graph Data Management and Mining: A Survey of Algorithms and Applications13Charu C. Aggarwal and Haixun Wang1. Introduction ... Conclusions and Future Research 55References 553Graph Mining: Laws and Generators69Deepayan Chakrabarti, Christos Faloutsos and Mary McGlohon1. Introduction 702. Graph Patterns 71x MANAGING AND ... Beijingviii MANAGING AND MINING GRAPH DATA 6. Vector Space Embeddings of Graphs via Graph Matching 2357. Conclusions 239References 2408A Survey of Algorithms for Keyword Search on Graph Data 249Haixun...
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Managing and Mining Graph Data part 5 pptx
... both the database and the IR communities.Graph is a general structure and it can be used to model a variety of complex data, including relational data and XML data. Because the underlying data assumes ... is to build a24 MANAGING AND MINING GRAPH DATA [94], random walk kernels [81] and diffusion kernels [119]. In random walkkernels [81], we attempt to determine the number of random walks betweenthe ... nodes in the graph independently and perform random walks starting from these nodes. These random walks can beGraph Data Management and Mining: A Survey of Algorithms and Applications 29used in...
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Managing and Mining Graph Data part 7 pptx
... transition any webpage in the collection uniformly at random.50 MANAGING AND MINING GRAPH DATA examine the problem of community detection and change detection in a singleframework. This provides ... relationship (SAR) princi-46 MANAGING AND MINING GRAPH DATA Let 𝐴 be the set of edges in the graph. Let 𝜋𝑖denote the steady state proba-bility of node 𝑖 in a random walk, and let 𝑃 = [𝑝𝑖𝑗] denote ... dissemination in the underlyingGraph Data Management and Mining: A Survey of Algorithms and Applications 41Densification: Most real networks such as the web and social networks con-tinue to become...
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Managing and Mining Graph Data part 8 ppt
... methods, procedures and functions in the program arenodes, and the relationships between the different methods are definedas edges. It is also possible to define nodes for data elements and modelrelationships ... graphs are created during program execution, and theyrepresent the invocation structure. For example, a call from one pro-56 MANAGING AND MINING GRAPH DATA [10] R. Agrawal, A. Borgida, H.V. Jagadish. ... of simple methods.60 MANAGING AND MINING GRAPH DATA [75] M. Fiedler, C. Borgelt. Support computation for mining frequent sub-graphs in a single graph. Workshop on Mining and Learning with Graphs(MLG’07),...
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Managing and Mining Graph Data part 11 ppt
... generators, we provide citations and a summary.3.1 Random Graph ModelsRandom graphs are generated by picking nodes under some random prob-ability distribution and then connecting them by edges. ... R«enyi in the 1960s [40, 41]. Their random graphmodel was the first and the simplest model for generating a graph.Description and Properties. We start with 𝑁 nodes, and for every pair ofnodes, an ... point represents a node and the 𝑥 and 𝑦 coordinates areits degree and total weight, respectively. To achieve a good fit, we bucketizethe 𝑥 axis with logarithmic binning [64], and, for each bin, we...
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Managing and Mining Graph Data part 13 pptx
... 104 MANAGING AND MINING GRAPH DATA where 𝑑𝑖𝑗is the distance between nodes 𝑖 and 𝑗, ℎ𝑗is some measure of the“centrality” of node 𝑗, and 𝛼 is a constant that controls ... devastating.110 MANAGING AND MINING GRAPH DATA The recursive nature of the partitions means that we automaticallyget sub-communities within existing communities (say, “RedHat” and “Mandrake” enthusiasts ... parameters as possible.There should be a fast parameter-fitting algorithm.102 MANAGING AND MINING GRAPH DATA Description and properties:. As an example, suppose we have a for-est which is prone...
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báo cáo hóa học:" L^{infty} estimates of solutions for the quasilinear parabolic equation with nonlinear gradient term and L^1 data" ppt
... comments and suggestions.References[1] Andreu, F, Segura de le´on, S, Toledo, J: Quasilinear diffusion equations with gradient terms and L1 data. Nonlinear Anal. 56, 1175–1209 (2004)[2] Andreu, ... data u0(x)is investigated in Section 5.2 Preliminaries and main resultsLet Ω be a bounded domain in RNwith smooth boundary ∂Ω and · r, · 1,rdenote theSobolev space Lr(Ω) and ... p, q, α, β and the function g(u).(H1) the parameters α, β > 1, 0 ≤ p < q < m + 2 < N, p +α < q +β and q(α − 1) ≥ p(β −1),(H2) the function g(u) ∈ C1 and ∃K1≥ 0 and 0 ≤ ν...
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Managing and Mining Graph Data part 62 pdf
... biomolecular target’s chemical data analy-sis. In recent years, the trend has been to integrate chemical data with protein and genetic data (bioinformatics data) and analyze the problem over multipleproteins ... Graph Data Mining 601dustry has generated a wealth of protein-ligand activity data for large com-pound libraries against many biomolecular targets. The data has been system-atically collected and ... Classification, 40XML Clustering, 35, 291XML Indexing, 4, 17602 MANAGING AND MINING GRAPH DATA sent interactions between drugs and targets, and then used kernel regression tothe relationship among...
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Managing and Mining Graph Data part 2 docx
... sizes of the second and third-largestconnected components (CC2 and CC3) stabilize. We fo-cus on these next-largest connected components in (c).84xx MANAGING AND MINING GRAPH DATA 17.1 An unreduced ... Eqs.(2.5) and (2.6) are 0.7810 and 0.5217, respectively.49216.3 A toy example (reproduced from 61) 49616.4 Equivalence for Social Position 500xviii MANAGING AND MINING GRAPH DATA 7.3 Graph ... superlinearly-more money itdonates, and similarly, the more donations a candidategets, the more average amount-per-donation is received.Inset plots on (c) and (d) show 𝑖𝑤 and 𝑜𝑤 versus time.Note they...
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Managing and Mining Graph Data part 3 potx
... LLC 2010 C.C. Aggarwal and H. Wang (eds.), Managing and Mining Graph Data, Advances in Database Systems 40, DOI 10.1007/978-1-4419-6045-0_1, 6 MANAGING AND MINING GRAPH DATA In the second case, ... the web and social networks are defined on massive graphs4 MANAGING AND MINING GRAPH DATA Natural Properties of Real Graphs and Generators. In order to under-stand the various management and mining ... in the case of structured data than in the case of multi-dimensional data. The problem of managing graph data is related to the widely stud-ied field of managing XML data. Where possible, we will...
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Managing and Mining Graph Data part 4 ppsx
... graph data management and min-ing algorithms are required. This includes web data, social and computernetworking, biological and chemical data, and software bug localization.16 MANAGING AND ... algorithms and applications.2.1 Indexing and Query Processing TechniquesExisting database models and query languages, including the relational model and SQL, lack native support for advanced data ... localization and computer networking. In addition, many new kinds of data such as semi-© Springer Science+Business Media, LLC 2010 C.C. Aggarwal and H. Wang (eds.), Managing and Mining Graph Data, 13Advances...
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- are presented by nutrient category in appendix 13 analyses excluded extreme outliers and graphically reported data listed in appendix 15 summary results of the analysis comparing the content of nutrients and other substances from organically and convent
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