managing and sharing customer data ppt

... methods, procedures and functions in the program are nodes, and the relationships between the different methods are defined as edges It is also possible to define nodes for data elements and model relationships ... representation of the relationships between the different methods and data elements of a program Different kinds of edges are used to denote control and data dependencies The first step is to determine conditional ... Community Evolution in Data Streams SIAM Conference on Data Mining, 2005. Trang 6[10] R Agrawal, A Borgida, H.V Jagadish Efficient Maintenance of Tran-sitive Relationships in Large Data and Knowledge

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... tccode(𝑤) for the node 𝑤 in Trang 8𝐺↓and𝐺↑ In particular,𝑝𝑜↓(𝑤) and 𝑝𝑜↑(𝑤) indicate the postorder of 𝑤, and𝐼↓(𝑤) and 𝐼↑(𝑤) indicate the intervals of 𝑤, in 𝐺↓and𝐺↑, respectively Second, based on ... where 𝑢∈ 𝐺𝑖 and𝑣∈ 𝐺𝑗, and let 𝑉 (𝐺𝑖) and 𝑉 (𝐺𝑗) denote the sets of nodes in 𝐺𝑖 and𝐺𝑗 It is done using the following two operations For all𝑎∈ 𝑎𝑛𝑐𝑠(𝑢) ∩ 𝑉 (𝐺𝑖), 𝐿𝑜𝑢𝑡(𝑎)← 𝐿𝑜𝑢𝑡(𝑎)∪ 𝐿′ 𝑜𝑢𝑡(𝑢), and For ... cover [1] and the chain cover [24, 9] Both tree cover and chain cover coding schema answer reachability queries only using the predicates, 𝒫𝑡𝑐(, ) and𝒫𝑐(, ), respectively On the other hand, the

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... Advances in Database Systems 40, DOI 10.1007/978-1-4419-6045-0_9, 275 276 MANAGING AND MINING GRAPH DATA structured data and XML [2] can typically be represented as graphs. In partic- ular, XML 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, ... both the 𝑘-means and 𝑘-medoid algorithms, since it uses centrality notions in determination of subsequent seeds. 284 MANAGING AND MINING GRAPH DATA not be practical. In order to handle such cases,

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... 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_11, 337 338 MANAGING AND MINING GRAPH DATA Figure 11.1. Graph ... ⋅⋅⋅ , 𝑥 ℓ ) in 𝐺 and (𝑥 ′ 1 , ⋅⋅⋅ , 𝑥 ′ ℓ ) in 𝐺 ′ . Here, 𝑝 𝑠 , 𝑝 𝑡 , and 𝑝 𝑞 denote the initial, transition, and termination probability of nodes in graph 𝐺, and 𝑝 ′ 𝑠 , 𝑝 ′ 𝑡 , and 𝑝 ′ 𝑞 denote ... graph data processing. In graph classification and regression, we assume that the target values of a certain number of graphs or a certain part of a graph are available as a training dataset, and

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... structures from incomplete 554 MANAGING AND MINING GRAPH DATA or noisy data such as DNA microarray data by making use of knowledge about known glycan structures from KEGG GLYCAN database [62]. There is ... 550 MANAGING AND MINING GRAPH DATA pairs satisfying certain constraints. It is formed by folding the single-stranded RNA molecule back onto itself, and it provides a scaffold ... human microarray datasets and recurrent co-expression clusters are identified. Functional homogeneity of these clusters are validated based on ChIP-chip data and conserved motif data [115]. For

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... Query Language and Access Methods for Graph Databases, appears as a chapter in Managing and Mining Graph Data, ed. Charu Aggarwal, Springer, 2010. [97] H He, Querying and mining graph databases Ph.D ... and computer science. Keywords: Power laws, structure, generators © Springer Science+Business Media, LLC 2010 C.C Aggarwal and H Wang (eds.), Managing and Mining Graph Data, 69 Advances in Database ... 2002. [161] K Riesen, X Jiang, H Bunke Exact and Inexact Graph Matching: Methodology and Applications, appears as a chapter in Managing and Mining Graph Data, ed Charu Aggarwal, Springer, 2010. [162]

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... (or profit), resources (to prevent a risk from causing damage) and tolerance to risks. 102 MANAGING AND MINING GRAPH DATA Description and properties:. As an example, suppose we have a for- est which ... 𝑖𝑗 + ℎ 𝑗 (𝑗 < 𝑖) (3.18) 104 MANAGING AND MINING GRAPH DATA where 𝑑 𝑖𝑗 is the distance between nodes 𝑖 and 𝑗, ℎ 𝑗 is some measure of the “centrality” of node 𝑗, and 𝛼 is a constant that controls ... current degree of node 𝑣 and 𝑑(𝑢, 𝑣) is the Euclidean distance between the two nodes. The values 𝛼 and 𝜎 are parameters, with 𝛼 = 𝜎 = 1 108 MANAGING AND MINING GRAPH DATA giving the best fits

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... words, for any nodes 𝑋𝑖 and𝑋𝑗 in𝒜 and 𝑋𝑘 and𝑋ℓinℬ, we have nodes𝑋𝑖,𝑘and𝑋𝑗,ℓin the Kronecker product𝒞, and an edge connects them iff the edges(𝑋𝑖, 𝑋𝑗) and (𝑋𝑘, 𝑋ℓ) exist in𝒜 and ℬ The Kronecker product ... network value of customers In Conference of the ACM Special Interest Group on Knowl-edge Discovery and Data Mining, New York, NY, 2001 ACM Press. [35] Sergey N Dorogovtsev and Jos«e Fernando Mendes ... Rajagopalan, and Andrew Tomkins The web as a graph: Measurements, models and methods In International Computing and Combinatorics Conference, Berlin, Germany, 1999 Springer [52] Paul L Krapivsky and Sidney

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... V1.label = ’A’ AND V2.label = ’B’ AND V3.label = ’C’ AND V1.vid = E1.vid1 AND V1.vid = E3.vid1 AND V2.vid = E1.vid2 AND V2.vid = E2.vid1 AND V3.vid = E2.vid2 AND V3.vid = E3.vid2 AND V1.vid <> ... 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_4, 125 Trang 5126 MANAGING AND MINING GRAPH DATAKeywords: Graph ... set of terminals and nonter-minals, and a finite set of production rules A production rule consists of a Trang 9130 MANAGING AND MINING GRAPH DATAnonterminal on the left hand side and a sequence

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... GraphQL is contained in Datalog This is proved by translating graphs, graph patterns, and graph templates into facts and rules of Datalog Trang 8Theorem 4.6 (GraphQL ⊆ Datalog) For any GraphQL ... attributes and structures are clearly separate Figure 4.7 shows a sample graph that represents a paper (the graph has no edges) Node𝑣1 has two attributes “title” and “year” Nodes𝑣2 and𝑣3have a ... structures and attributes We use a matched graph to denote the binding between a graph pattern and a graph Definition 4.3 (Matched Graph) Given an injective mapping 𝜙 between a pat-tern 𝒫 and a graph

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... is the refinement level, 𝑑1 and 𝑑2 are maximum degrees of 𝒫 and 𝐺 respectively, and𝑀 () is the time complexity of maximum bipartite matching (𝑂(𝑛2.5) for Hopcroft and Karp’s algorithm [19]) Figure ... using neighborhood subgraphs and profiles The resulting search spaces are also shown for different pruning techniques. Figure 4.16 shows the sample graph pattern 𝒫 and the database graph 𝐺 again for ... 21 these pairs are marked and checked again (line 14) Second, the⟨𝑢, 𝑣⟩ pairs are stored and manipulated using a hashtable instead of a matrix This reduces the space and time complexity from𝑂(𝑘⋅

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... recently, XML databases have been studied intensively for tree-based data models and semistructured data XML databases can be generally im-plemented in two approaches: mapping to relational database ... semistructured and does not cast strict and pre-defined data types or schemas on nodes, edges, and graphs In contrast, SQL presumes a strict schema in order to store data OODB requires objects (nodes and ... GraphDB data model, the whole database is viewed as a single graph Objects in the database are strong-typed and the object types support inheritance Each object is associated with an object type and

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... fetching a candidate graph from the disk, and 𝑇 𝑖𝑠𝑜 𝑡𝑒𝑠𝑡 is the average time of checking a subgraph isomorphism, which is conducted over query 𝑄 and graphs in the candidate answer set. The candidate ... support constraint will select and index small structures with low minimum supports and large structures with high minimum supports. 166 MANAGING AND MINING GRAPH DATA This method has two advantages: ... effectively and efficiently used as indexing features for graph databases. It was observed that the majority of frequent graph patterns discovered in many applications 168 MANAGING AND MINING GRAPH DATA

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... 112–115, 2002 Trang 8[13] R Goldman and J Widom Dataguides: Enabling query formulation andoptimization in semistructured databases In Proc of 1997 Int Conf on Very Large Data Bases (VLDB’97), pages 436–445, ... CA, 1980 [25] E Petrakis and C Faloutsos Similarity searching in medical image data-bases Knowledge and Data Engineering, 9(3):435–447, 1997. [26] M Petrovic, H Liu, and H Jacobsen G-ToPSS: Fast ... J Wang, and R Giugno Algorithmics and applications oftree and graph searching In Proc of the 21th ACM Symp on Principles of Database Systems (PODS’02), pages 39–52, 2002. [29] A Shokoufandeh,

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... complex data as graphs and integrate data effectively The dominance of graphs in real-world applications demands new graph data management so that users can access graph data effectively and efficiently ... nodes, 𝑢 and 𝑣, are said to be in a strongly connected component, if and only if both𝑢 ↝ 𝑣 and 𝑣 ↝ 𝑢 are true And in a strongly connected component, for every two nodes, 𝑢 and 𝑣, 𝑢 ↝ 𝑣 and 𝑣 ↝ ... (or labels) to nodes in𝐺 later in detail in this survey, and use codes and labels interchangeably Let the codes for𝑢 and 𝑣 be code(𝑢) and code(𝑣) If the predicate𝒫(code(𝑢), code(𝑣)) is true,

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... 2-hop clusters based on 𝑤 ∈ 𝑊 and any nodes that connect via 204 MANAGING AND MINING GRAPH DATA 𝑤 are included in 𝐴 𝑤 and 𝐷 𝑤 . And all 𝑤 ∈ 𝑊 are added into 𝐿 𝑜𝑢𝑡 (𝑎) and 𝐿 𝑖𝑛 (𝑑). Upon the deletion ... identifies all pairs of nodes, 𝑣 𝑖 and 𝑣 𝑗 , such that (𝑣 𝑖 , 𝑣 𝑗 ) ∈ 𝐺 𝐷 , label(𝑣 𝑖 ) = 𝐴, and label(𝑣 𝑗 ) = 𝐷. An edge (𝐴, 𝐷) ∈ 𝐸(𝐺 𝑞 ) 208 MANAGING AND MINING GRAPH DATA represents a reachability ... 𝑤) > 𝛿(𝑎, 𝑤). 206 MANAGING AND MINING GRAPH DATA w D w A w G i x 1 x d x 2 a A 2-hop cluster in PSG Figure 6.11. The 2-hop Distance Aware Cover (Figure 2 in [10]) Cheng and Yu in [10] discuss

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... 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_7, 217 Trang 6218 MANAGING AND MINING GRAPH DATA1 Introduction ... node labeling function, and Trang 8220 MANAGING AND MINING GRAPH DATAa b c d e f g (d) Figure 7.1 Different kinds of graphs: (a) undirected and unlabeled, (b) directed and unlabeled, (c) undirected ... 1988. [32] S TrißI and U Leser Fast and practical indexing and querying of very large graphs In Proceedings of the 2007 ACM SIGMOD international conference on Management of data (SIGMOD 2007),

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... 1 2. Graph Management and Mining Applications 3 3. Summary 8 References 9 2 Graph Data Management and Mining: A Survey of Algorithms and Applications 13 Charu C. Aggarwal and Haixun Wang 1. Introduction ... Conclusions and Future Research 55 References 55 3 Graph Mining: Laws and Generators 69 Deepayan Chakrabarti, Christos Faloutsos and Mary McGlohon 1. Introduction 70 2. Graph Patterns 71 x MANAGING AND ... Beijing viii MANAGING AND MINING GRAPH DATA 6. Vector Space Embeddings of Graphs via Graph Matching 235 7. Conclusions 239 References 240 8 A Survey of Algorithms for Keyword Search on Graph Data 249 Haixun...

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... 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 a 24 MANAGING AND MINING GRAPH DATA [94], random walk kernels [81] and diffusion kernels [119]. In random walk kernels [81], we attempt to determine the number of random walks between the ... nodes in the graph independently and perform random walks starting from these nodes. These random walks can be Graph Data Management and Mining: A Survey of Algorithms and Applications 29 used in...

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