LEADER 02779nam 2200457 450 001 996418301103316 005 20210219124226.0 010 $a3-030-59410-6 024 7 $a10.1007/978-3-030-59410-7 035 $a(CKB)4100000011469470 035 $a(MiAaPQ)EBC6355589 035 $a(DE-He213)978-3-030-59410-7 035 $a(PPN)255180586 035 $a(EXLCZ)994100000011469470 100 $a20210219d2020 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDatabase systems for advanced applications$hPart I $e25th International Conference, DASFAA 2020, Jeju, South Korea, September 24-27, 2020, proceedings /$fYunmook Nah [and five others] editors 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$dİ2020 215 $a1 online resource (XXXIV, 809 p. 351 illus., 250 illus. in color.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v12112 311 $a3-030-59409-2 327 $aAdvanced database and web applications.-Big data -- Data mining -- Machine learning for database -- Data warehouse and OLAP -- Information retrieval -- Data model and query language -- Query processing -- Optimization -- Recommendation systems -- Data quality and credibility -- Multimedia databases -- Temporal and spatial databases -- Data streams and time-series data -- Semantic web and knowledge management -- Graph data management -- Social network analytics -- Bio and health informatics -- Blockchain and parallel/distributed systems -- Security -- Privacy and Trust -- Databases for emerging hardware. 330 $aThe 4 volume set LNCS 12112-12114 constitutes the papers of the 25th International Conference on Database Systems for Advanced Applications which will be held online in September 2020. The 119 full papers presented together with 19 short papers plus 15 demo papers and 4 industrial papers in this volume were carefully reviewed and selected from a total of 487 submissions. The conference program presents the state-of-the-art R&D activities in database systems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v12112 606 $aDatabase management$vCongresses 615 0$aDatabase management 676 $a005.74 702 $aNah$b Yunmook 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418301103316 996 $aDatabase Systems for Advanced Applications$9772450 997 $aUNISA LEADER 04004nam 22007455 450 001 9910410058603321 005 20251225181927.0 010 $a3-030-49210-9 024 7 $a10.1007/978-3-030-49210-6 035 $a(CKB)5280000000218578 035 $a(MiAaPQ)EBC6297263 035 $a(DE-He213)978-3-030-49210-6 035 $a(PPN)248595083 035 $a(MiAaPQ)EBC6221052 035 $a(EXLCZ)995280000000218578 100 $a20200602d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInductive Logic Programming $e29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3?5, 2019, Proceedings /$fedited by Dimitar Kazakov, Can Erten 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (154 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v11770 311 08$a3-030-49209-5 327 $aCONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data. 330 $aThis book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. 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