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Database Systems for Advanced Applications: 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22–25, 2019, Proceedings, Part I de Guoliang Li

Database Systems for Advanced Applications: 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22–25, 2019, Proceedings, Part I: Lecture Notes in Computer Science, cartea 11446

Editat de Guoliang Li, Jun Yang, Joao Gama, Juggapong Natwichai, Yongxin Tong
en Limba Engleză Paperback – 25 apr 2019
This two-volume set LNCS 11446 and LNCS 11447 constitutes the refereed proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019.
The 92 full papers and 64 short papers were carefully selected from a total of 501 submissions. In addition, 13 demo papers and 6 tutorial papers are included. The full papers are organized in the following topics: big data; clustering and classification; crowdsourcing; data integration; embedding; graphs; knowledge graph; machine learning; privacy and graph; recommendation; social network; spatial; and spatio-temporal. The short papers, demo papers, and tutorial papers can be found in the volume LNCS 11448, which also includes the workshops of DASFAA 2019.

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Specificații

ISBN-13: 9783030185756
ISBN-10: 3030185753
Pagini: 760
Ilustrații: XXIV, 817 p.
Dimensiuni: 155 x 235 mm
Greutate: 1.16 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Information Systems and Applications, incl. Internet/Web, and HCI

Locul publicării:Cham, Switzerland

Cuprins

Big Data.- Accelerating Real-time Tracking Applications over Big Data Stream with Constrained Space.- A Frequency Scaling based Performance Indicator Framework for Big Data Systems.- A Time-Series Sockpuppet Detection Method for Dynamic Social Relationships.- Accelerating Hybrid Transactional/Analytical Processing using  Consistent Dual-Snapshot.- HSDS: an Abstractive Model for Automatic Survey Generation.- PU-Shapelets: Towards Pattern-based Positive Unlabeled Classification of Time Series.- Clustering and Classification.- Discovering Relationship Patterns among Associated Temporal Event Sequences.- Efficient Mining of Event Periodicity in Data Series.- EPPADS: An Enhanced Phase-based Performance-Aware Dynamic Scheduler for High Job Execution Performance in Large Scale Data Clusters.- Incremental Discovery of Order Dependencies on Tuple Insertions.- Multi-view Spectral Clustering via Weighted-view Consensus Similarity and Matrix-decomposition based Discretization.-SIRCS: Slope-intercept-residual Compression by Correlation Sequencing for Multi-stream High Variation Data.- Crowdsourcing.- Fast Quorum-based Log Replication and Replay for Fast Databases.- PDCS: A Privacy-preserving Distinct Counting Scheme for Mobile Sensing.- Reinforced Reliable Worker Selection for Spatial Crowdsensing Networks.- SeqST-ResNet: A Sequential Spatial Temporal ResNet for Task Prediction in Spatial Crowdsourcing.- Towards Robust Arbitrarily Oriented Subspace Clustering.- Truthful Crowdsensed Data Trading Based on Reverse Auction and Blockchain.- Data Integration.- Selective Matrix Factorization for Multi-Relational Data Fusion.- Selectivity Estimation on Set Containment Search.- Typicality-based Across-time Mapping of Entity Sets in Document Archives.- Unsupervised Entity Alignment using Attribute Triples and Relation Triples.- Combining Meta-Graph and Attention for Recommendation over Heterogeneous Information Network.- Efficient Search of the Most Cohesive Co-Located Community in Attributed Networks.- Embedding.- A Weighted Word Embedding Model for Text Classification.- Bipartite Network Embedding via Effective Integration of Explicit and Implicit Relations.- Enhancing Network Embedding with Implicit Clustering.- MDAL: Multi-task Dual Attention LSTM Model for Semi-supervised Network Embedding.- Net2Text: An Edge Labelling Language Model for Personalized Comment Generation.- Understanding Information Diffusion via Heterogeneous Information Network Embeddings.- Graphs.- Distributed Parallel Structural Hole Detection on Big Graphs.- DynGraphGAN: Dynamic Graph Embedding via Generative Adversarial Networks.- Evaluating Mixed Patterns on Large Data Graphs Using Bitmap Views.- Heterogeneous Information Network Hashing for Fast Nearest Neighbor Search.- Learning Fine-grained Patient Similarity with Dynamic Bayesian Network Embedded RNNs.- Towards Efficient k-TriPeak Decomposition on Large Graphs.- Knowledge Graph.- Evaluating the Choice of Tags in CQA Sites.- Fast Maximal Clique Enumeration for Real-world Graphs.- Leveraging Knowledge Graph Embeddings for Natural Language Question Answering.- Measuring Semantic Relatedness with Knowledge Association Network.- SINE: Side Information Network Embedding.- A Knowledge Graph Enhanced Topic Modeling Approach for Herb Recommendation.- Knowledge Base Error Detection with Relation Sensitive Embedding.- Leon: A Distributed RDF Engine for Multi-query Processing.- MathGraph: A knowledge graph for solving mathematical exercises.- Multi-Hop Path Queries over Knowledge Graphs with Neural Memory Networks.- Sentiment Classification by Leveraging the Shared Knowledge.