BibSLEIGH — Proceedings of the 45th International Conference on Very Large Data Bases

Proceedings of the 45th International Conference on Very Large Data Bases
VLDB-2019, 2019.

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@proceedings{VLDB-2019,
	journal       = "{Proceedings of the VLDB Endowment}",
	title         = "{Proceedings of the 45th International Conference on Very Large Data Bases}",
	volume        = 12,
	year          = 2019,
}

Contents (221 items)

VLDB-2019-KimLHE18: List Intersection for Web Search: Algorithms, Cost Models, and Optimizations (SK, TL, SwH, SE), pp. 1–13.
VLDB-2019-WhittakerH18: Interactive Checks for Coordination Avoidance (MW, JMH), pp. 14–27.
VLDB-2019-QinX18: Pigeonring: A Principle for Faster Thresholded Similarity Search (JQ, CX), pp. 28–42.
VLDB-2019-SariyuceSP18: Local Algorithms for Hierarchical Dense Subgraph Discovery (AES, CS, AP), pp. 43–56.
VLDB-2019-YangFWLLD18: Cost-Effective Data Annotation using Game-Based Crowdsourcing (JY, JF, ZW, GL0, TL, XD0), pp. 57–70.
VLDB-2019-HuangPPALD18: Optimization for Active Learning-based Interactive Database Exploration (EH, LP, LDP, AA, AL, YD), pp. 71–84.
VLDB-2019-BleifussBJKNS18: Exploring Change - A New Dimension of Data Analytics (TB, LB, TJ, DVK, FN, DS), pp. 85–98.
VLDB-2019-GhoshACHSL18: The Flexible Socio Spatial Group Queries (BG, MEA, FMC, SHA, TS, JL0), pp. 99–111.
VLDB-2019-EchihabiZPB18: The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art (KE, KZ, TP, HB), pp. 112–127.
VLDB-2019-WangWGZCNOS18: Rafiki: Machine Learning as an Analytics Service System (WW0, JG, MZ, SW, GC0, TKN, BCO, JS, MR), pp. 128–140.
VLDB-2019-SuboticJCFS18: Automatic Index Selection for Large-Scale Datalog Computation (PS, HJ, LC, AF, BS), pp. 141–153.
VLDB-2019-SongLWGLJ18: Start Late or Finish Early: A Distributed Graph Processing System with Redundancy Reduction (SS, XL0, QW, AG, TL0, LKJ), pp. 154–168.
VLDB-2019-DingKG18: Improving Optimistic Concurrency Control Through Transaction Batching and Operation Reordering (BD, LK, JG), pp. 169–182.
VLDB-2019-XieCK18: Query Log Compression for Workload Analytics (TX, VC, OK), pp. 183–196.
VLDB-2019-AliEACCS18: The Maximum Trajectory Coverage Query in Spatial Databases (MEA, SSE, KA, FMC, JSC, TS), pp. 197–209.
VLDB-2019-WuJAPLQR18: Towards a Learning Optimizer for Shared Clouds (CW, AJ, SA, HP, WL, SQ, SR), pp. 210–222.
VLDB-2019-VarmaR18: Snuba: Automating Weak Supervision to Label Training Data (PV, CR), pp. 223–236.
VLDB-2019-AsudehJMS18: On Obtaining Stable Rankings (AA, HVJ, GM, JS), pp. 237–250.
VLDB-2019-JiJ18: PS-Tree-based Efficient Boolean Expression Matching for High Dimensional and Dense Workloads (SJ, HAJ), pp. 251–264.
VLDB-2019-YanCMR18: SWIFT: Mining Representative Patterns from Large Event Streams (YY, LC0, SM, EAR), pp. 265–277.
VLDB-2019-CADA18: Smurf: Self-Service String Matching Using Random Forests (PSGC, AA, AD, AA), pp. 278–291.
VLDB-2019-LiuSBS18: Chasing Similarity: Distribution-aware Aggregation Scheduling (FL, AS, SB, AS), pp. 292–306.
VLDB-2019-BaterHEMR18: ShrinkWrap: Efficient SQL Query Processing in Differentially Private Data Federations (JB, XH0, WE, AM, JR), pp. 307–320.
VLDB-2019-GillDHP18: A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms (GG, RD, LH, KP), pp. 321–334.
VLDB-2019-KumarE18: Utility-Driven Graph Summarization (KAK, PE), pp. 335–347.
VLDB-2019-KaraEZA18: ColumnML: Column-Store Machine Learning with On-The-Fly Data Transformation (KK, KE, CZ, GA), pp. 348–361.
VLDB-2019-LiSDW18: Cost-efficient Data Acquisition on Online Data Marketplaces for Correlation Analysis (YL, HS, BD, WHW), pp. 362–375.
VLDB-2019-DolatshahTWP18: Cleaning Crowdsourced Labels Using Oracles For Statistical Classification (MD, MT, JW, JP), pp. 376–389.
VLDB-2019-LissandriniBV18: Beyond Macrobenchmarks: Microbenchmark-based Graph Database Evaluation (ML, MB, YV), pp. 390–403.
VLDB-2019-BalegasDFRP18: IPA: Invariant-preserving Applications for Weakly consistent Replicated Databases (VB, SD, CF0, RR, NMP), pp. 404–418.
VLDB-2019-AbuzaidKSGXSASM18: DIFF: A Relational Interface for Large-Scale Data Explanation (FA, PK, SS, EG, EX, AS, AA, JS, EM0, XW0, JFN, PB, MZ), pp. 419–432.
VLDB-2019-Ben-BasatFS18: Stream Frequency Over Interval Queries (RBB, RF, RS), pp. 433–445.
VLDB-2019-XinMMLSP18: Helix: Holistic Optimization for Accelerating Iterative Machine Learning (DX, SM, LM, JL, SS, AGP), pp. 446–460.
VLDB-2019-FuXWC: Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph (CF, CX, CW, DC), pp. 461–474.
VLDB-2019-WangS: Document Reordering for Faster Intersection (QW, TS), pp. 475–487.
VLDB-2019-ZhangO: Correlation Constraint Shortest Path over Large Multi-Relation Graphs (XZ, MTÖ), pp. 488–501.
VLDB-2019-LangNKB: Performance-Optimal Filtering: Bloom overtakes Cuckoo at High-Throughput (HL, TN0, AK, PAB), pp. 502–515.
VLDB-2019-ZeuchBRMKLRTM: Analyzing Efficient Stream Processing on Modern Hardware (SZ, SB, TR, BDM, JK, CL, MR, JT, VM), pp. 516–530.
VLDB-2019-LuoC: Efficient Data Ingestion and Query Processing for LSM-Based Storage Systems (CL, MJC0), pp. 531–543.
VLDB-2019-ChrysogelosKAA: HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines (PC, MK, RA, AA), pp. 544–556.
VLDB-2019-AtzeniBPT: Meta-Mappings for Schema Mapping Reuse (PA, LB, PP, RT), pp. 557–569.
VLDB-2019-XuGDWW: An Experimental Evaluation of Garbage Collectors on Big Data Applications (LX, TG0, WD, WW0, JW0), pp. 570–583.
VLDB-2019-GuoCWQZ: Adaptive Optimistic Concurrency Control for Heterogeneous Workloads (JG, PC, JW, WQ, AZ), pp. 584–596.
VLDB-2019-LinPLTEW: MgCrab: Transaction Crabbing for Live Migration in Deterministic Database Systems (YSL, SKP, MKL, CT, AJE, SHW), pp. 597–610.
VLDB-2019-MaiyyaNAA: Unifying Consensus and Atomic Commitment for Effective Cloud Data Management (SM, FN, DA, AEA), pp. 611–623.
VLDB-2019-WuSH: Autoscaling Tiered Cloud Storage in Anna (CW, VS, JMH), pp. 624–638.
VLDB-2019-DignosGNGB: Snapshot Semantics for Temporal Multiset Relations (AD, BG, XN, JG, MHB), pp. 639–652.
VLDB-2019-KwashieLLLSY: Certus: An Effective Entity Resolution Approach with Graph Differential Dependencies (GDDs) (SK, JL, JL, LL0, MS, LY), pp. 653–666.
VLDB-2019-HanGXTHCH: Efficient and Effective Algorithms for Clustering Uncertain Graphs (KH, FG, XX, JT0, YH, ZC, HH0), pp. 667–680.
VLDB-2019-ZouIJ: Pangea: Monolithic Distributed Storage for Data Analytics (JZ0, AI, CJ), pp. 681–694.
VLDB-2019-FanZZAKP: Scaling-Up In-Memory Datalog Processing: Observations and Techniques (ZF, JZ, ZZ, AA, PK, JMP), pp. 695–708.
VLDB-2019-ArcherABMSYZ: Cache-aware load balancing of data center applications (AA, KA, MB, VSM, AS, RY, RZ), pp. 709–723.
VLDB-2019-BorkowskiHS: Minimizing Cost by Reducing Scaling Operations in Distributed Stream Processing (MB, CH, SS0), pp. 724–737.
VLDB-2019-WuADMD: ProvCite: Provenance-based Data Citation (YW, AA, DD, TM, SBD), pp. 738–751.
VLDB-2019-FanLTZ: Deducing Certain Fixes to Graphs (WF, PL, CT, JZ), pp. 752–765.
VLDB-2019-CeccarelloPP: Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially (MC, AP, GP), pp. 766–778.
VLDB-2019-WangM: Explain3D: Explaining Disagreements in Disjoint Datasets (XW, AM), pp. 779–792.
VLDB-2019-WonKYTS: DASH: Database Shadowing for Mobile DBMS (YW, SK, JY, DT, JS0), pp. 793–806.
VLDB-2019-WangKZAZM: Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (ZW, KK, HZ, GA, CZ, OM), pp. 807–821.
VLDB-2019-JankovLYCZJG: Declarative Recursive Computation on an RDBMS (DJ, SL, BY, ZC, JZ0, CJ, ZJG), pp. 822–835.
VLDB-2019-Ghandeharizadeh: Design, Implementation, and Evaluation of Write-Back Policy with Cache Augmented Data Stores (SG, HN), pp. 836–849.
VLDB-2019-NguyenYWZNS: User Guidance for Efficient Fact Checking (TTN, HY, MW, BZ, QVHN, BS), pp. 850–863.
VLDB-2019-KeKQ: An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs (XK, AK, LLHQ), pp. 864–876.
VLDB-2019-FanHLLYZ: Dynamic Scaling for Parallel Graph Computations (WF, CH, ML, PL, QY, JZ), pp. 877–890.
VLDB-2019-LiZWT: TopoX: Topology Refactorization for Efficient Graph Partitioning and Processing (DL, YZ, JW, KLT), pp. 891–905.
VLDB-2019-AvdiukhinPY: Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent (DA, SP, GY), pp. 906–919.
VLDB-2019-CaoYMRG: Efficient Discovery of Sequence Outlier Patterns (LC0, YY, SM, EAR, MG), pp. 920–932.
VLDB-2019-BogatovKR: A Comparative Evaluation of Order-Revealing Encryption Schemes and Secure Range-Query Protocols (DB, GK, LR), pp. 933–947.
VLDB-2019-OrakzaiCP: k/2-hop: Fast Mining of Convoy Patterns With Effective Pruning (FO, TC, TBP), pp. 948–960.
VLDB-2019-SunS0BD: Balance-Aware Distributed String Similarity-Based Query Processing System (JS, ZS, GL0, ZB, DD), pp. 961–974.
VLDB-2019-Ruan0DLOZ: Fine-Grained, Secure and Efficient Data Provenance for Blockchain (PR, GC0, AD, QL, BCO, MZ), pp. 975–988.
VLDB-2019-ChoiPC: Progressive Top-k Subarray Query Processing in Array Databases (DC, CSP, YDC), pp. 989–1001.
VLDB-2019-HoffmannLMKLR: Megaphone: Latency-conscious state migration for distributed streaming dataflows (MH, AL, FM, VK, JL, TR), pp. 1002–1015.
VLDB-2019-NguyenWZYNS: From Anomaly Detection to Rumour Detection using Data Streams of Social Platforms (TTN, MW, BZ, HY, QVHN, BS), pp. 1016–1029.
VLDB-2019-GuptaLMP0A: Obscure: Information-Theoretic Oblivious and Verifiable Aggregation Queries (PG, YL, SM, NP, SS0, SA), pp. 1030–1043.
VLDB-2019-DuttWNKNC: Selectivity Estimation for Range Predicates using Lightweight Models (AD, CW0, AN, SK, VRN, SC), pp. 1044–1057.
VLDB-2019-YuanLWMW: Constrained Shortest Path Query in a Large Time-Dependent Graph (YY0, XL, GW, YM, YW), pp. 1058–1070.
VLDB-2019-ChuWPZYC: Finding Theme Communities from Database Networks (LC, ZW, JP, YZ, YY0, EC), pp. 1071–1084.
VLDB-2019-PanLH: Ridesharing: Simulator, Benchmark, and Evaluation (JP, GL0, JH), pp. 1085–1098.
VLDB-2019-LaiQYJLWHLQZZQZ: Distributed Subgraph Matching on Timely Dataflow (LL, ZQ, ZY, XJ, ZL, RW0, KH, XL0, LQ, WZ0, YZ0, ZQ, JZ), pp. 1099–1112.
VLDB-2019-QiaoNSFPE: Hyper Dimension Shuffle: Efficient Data Repartition at Petabyte Scale in Scope (SQ, AN, JS, MF, HP, JE), pp. 1113–1125.
VLDB-2019-CormodeKS: Answering Range Queries Under Local Differential Privacy (GC, TK, DS), pp. 1126–1138.
VLDB-2019-WangLQZZ: Vertex Priority Based Butterfly Counting for Large-scale Bipartite Networks (KW, XL0, LQ, WZ0, YZ0), pp. 1139–1152.
VLDB-2019-CaoFY: Block as a Value for SQL over NoSQL (YC0, WF, TY), pp. 1153–1166.
VLDB-2019-TangwongsanHS: Optimal and General Out-of-Order Sliding-Window Aggregation (KT, MH, SS0), pp. 1167–1180.
VLDB-2019-TangMYC: Creating Top Ranking Options in the Continuous Option and Preference Space (BT, KM, MLY, ZC), pp. 1181–1194.
VLDB-2019-MaLWCP: Ontology-based Entity Matching in Attributed Graphs (HM, MAL, YW, FC, JP), pp. 1195–1207.
VLDB-2019-ChenGFMJG: Real-time Distributed Co-Movement Pattern Detection on Streaming Trajectories (LC0, YG, ZF, XM, CSJ, CG), pp. 1208–1220.
VLDB-2019-TanZLCZZQSCZ: iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases (JT, TZ, FL0, JC, QZ, PZ, HQ, YS, WC, RZ), pp. 1221–1234.
VLDB-2019-WhittakerETWN: Online Template Induction for Machine-Generated Emails (MJW, NE, ST, JBW, MN), pp. 1235–1248.
VLDB-2019-WangLT: Querying Shortest Paths on Time Dependent Road Networks (YW, GL0, NT0), pp. 1249–1261.
VLDB-2019-FarihaM: Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity (AF, AM), pp. 1262–1275.
VLDB-2019-ZhouANHX: Automated Verification of Query Equivalence Using Satisfiability Modulo Theories (QZ, JA, SBN, WH, DX), pp. 1276–1288.
VLDB-2019-XuL: Towards a Unified Framework for String Similarity Joins (PX0, JL), pp. 1289–1302.
VLDB-2019-YoonLL: NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing (SY, JGL0, BSL), pp. 1303–1315.
VLDB-2019-LuYM: STAR: Scaling Transactions through Asymmetric Replication (YL, XY, SM), pp. 1316–1329.
VLDB-2019-LiFLMHLT: Subjective Databases (YL, AF, JL, SM, AYH, VL, WCT), pp. 1330–1343.
VLDB-2019-RenWHY: Fast and Robust Distributed Subgraph Enumeration (XR, JW, WSH, JXY), pp. 1344–1356.
VLDB-2019-FuJSC: An Experimental Evaluation of Large Scale GBDT Systems (FF, JJ, YS, BC0), pp. 1357–1370.
VLDB-2019-KotsogiannisTHF: PrivateSQL: A Differentially Private SQL Query Engine (IK, YT, XH0, MF, AM, MH, GM), pp. 1371–1384.
VLDB-2019-AmiriAA: CAPER: A Cross-Application Permissioned Blockchain (MJA, DA, AEA), pp. 1385–1398.
VLDB-2019-KoliousisWWMCP: Crossbow: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers (AK, PW, MW, LM, PC, PRP), pp. 1399–1413.
VLDB-2019-FengCJG: Finding Attribute-Aware Similar Region for Data Analysis (KF, GC, CSJ, TG), pp. 1414–1426.
VLDB-2019-TangSEKF: Intermittent Query Processing (DT, ZS, AJE, SK, MJF), pp. 1427–1441.
VLDB-2019-BudiuGSWKA: Hillview: A trillion-cell spreadsheet for big data (MB, PG, LS, UW, HK, MKA), pp. 1442–1457.
VLDB-2019-WeiL: Embedded Functional Dependencies and Data-completeness Tailored Database Design (ZW, SL), pp. 1458–1470.
VLDB-2019-FanG: Ocean Vista: Gossip-Based Visibility Control for Speedy Geo-Distributed Transactions (HF, WG), pp. 1471–1484.
VLDB-2019-WangC: An IDEA: An Ingestion Framework for Data Enrichment in AsterixDB (XW, MJC0), pp. 1485–1498.
VLDB-2019-KaryakinS: DimmStore: Memory Power Optimization for Database Systems (AK, KS), pp. 1499–1512.
VLDB-2019-YanC: Generating Application-specific Data Layouts for In-memory Databases (CY, AC), pp. 1513–1525.
VLDB-2019-HaiQ: Rewriting of Plain SO Tgds into Nested Tgds (RH, CQ), pp. 1526–1538.
VLDB-2019-NathanGSSJ: Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database (SN, CG, AS, MS, PJ), pp. 1539–1552.
VLDB-2019-KunftKSBRM: An Intermediate Representation for Optimizing Machine Learning Pipelines (AK, AK, SS, SB, TR, VM), pp. 1553–1567.
VLDB-2019-FangZC: Accelerating Raw Data Analysis with the ACCORDA Software and Hardware Architecture (YF, CZ, AAC), pp. 1568–1582.
VLDB-2019-SiddiqueEH: Comparing Synopsis Techniques for Approximate Spatial Data Analysis (ABS, AE, VH), pp. 1583–1596.
VLDB-2019-El-HindiBAKR: BlockchainDB - A Shared Database on Blockchains (MEH, CB, AA, DK, RR), pp. 1597–1609.
VLDB-2019-JiaDWHGLZSS: Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms (RJ, DD, BW, FAH, NMG, BL0, CZ, CJS, DS), pp. 1610–1623.
VLDB-2019-SaxenaGI: Distributed Implementations of Dependency Discovery Algorithms (HS, LG, IFI), pp. 1624–1636.
VLDB-2019-ZamanianYSK: Rethinking Database High Availability with RDMA Networks (EZ, XY, MS, TK), pp. 1637–1650.
VLDB-2019-BressanLP: Motivo: Fast Motif Counting via Succinct Color Coding and Adaptive Sampling (MB0, SL0, AP), pp. 1651–1663.
VLDB-2019-PoddarBP: Arx: An Encrypted Database using Semantically Secure Encryption (RP, TB, RAP), pp. 1664–1678.
VLDB-2019-GaoLXSDY: Efficient Knowledge Graph Accuracy Evaluation (JG, XL, YEX, BS, XLD, JY), pp. 1679–1691.
VLDB-2019-MhedhbiS: Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins (AM, SS), pp. 1692–1704.
VLDB-2019-MarcusNMZAKPT: Neo: A Learned Query Optimizer (RCM, PN, HM, CZ, MA, TK, OP, NT), pp. 1705–1718.
VLDB-2019-FangYCLL: Efficient Algorithms for Densest Subgraph Discovery (YF, KY, RC, LVSL, XL0), pp. 1719–1732.
VLDB-2019-MarcusP: Plan-Structured Deep Neural Network Models for Query Performance Prediction (RCM, OP), pp. 1733–1746.
VLDB-2019-RenLA: SLOG: Serializable, Low-latency, Geo-replicated Transactions (KR, DL, DJA), pp. 1747–1761.
VLDB-2019-PaparrizosF: GRAIL: Efficient Time-Series Representation Learning (JP, MJF), pp. 1762–1777.
VLDB-2019-DamasioBCGMSZ: GALO: Guided Automated Learning for re-Optimization (GD, SB, VC, PG, PM, JS, CZ), pp. 1778–1781.
VLDB-2019-TianTPSXZ: Synergistic Graph and SQL Analytics Inside IBM Db2 (YT, ST, MHP, WS, ELX, WZ), pp. 1782–1785.
VLDB-2019-DingWSLLG: Cleanits: A Data Cleaning System for Industrial Time Series (XD, HW, JS, ZL, JL, HG), pp. 1786–1789.
VLDB-2019-ZhangBMLZ: ITAA: An Intelligent Trajectory-driven Outdoor Advertising Deployment Assistant (YZ, ZB, SM, YL, YZ), pp. 1790–1793.
VLDB-2019-QianPS: SystemER: A Human-in-the-loop System for Explainable Entity Resolution (KQ0, LP0, PS), pp. 1794–1797.
VLDB-2019-HuynhP: Buckle: Evaluating Fact Checking Algorithms Built on Knowledge Bases (VPH, PP), pp. 1798–1801.
VLDB-2019-GaoXLJXKM: A Query System for Efficiently Investigating Complex Attack Behaviors for Enterprise Security (PG, XX, ZL, KJ, FX, SRK, PM), pp. 1802–1805.
VLDB-2019-MiaoZLGKR: CAPE: Explaining Outliers by Counterbalancing (ZM, QZ, CL, BG, OK, SR), pp. 1806–1809.
VLDB-2019-RamachandraP: BlackMagic: Automatic Inlining of Scalar UDFs into SQL Queries with Froid (KR0, KP), pp. 1810–1813.
VLDB-2019-BergZBR: ProgressiveDB - Progressive Data Analytics as a Middleware (LB, TZ0, CB, UR), pp. 1814–1817.
VLDB-2019-KaraWZA: doppioDB 2.0: Hardware Techniques for Improved Integration of Machine Learning into Databases (KK, ZW, CZ, GA), pp. 1818–1821.
VLDB-2019-PahinsOASPBC: COVIZ: A System for Visual Formation and Exploration of Patient Cohorts (CALP, BOT, SAY, VS, JLP, JCB, JC), pp. 1822–1825.
VLDB-2019-FrankeSR: PRIMAT: A Toolbox for Fast Privacy-preserving Matching (MF, ZS, ER), pp. 1826–1829.
VLDB-2019-MarcusZYKP: NashDB: Fragmentation, Replication, and Provisioning using Economic Methods (RM, CZ, SY, GK, OP), pp. 1830–1833.
VLDB-2019-SabekMM: Flash in Action: Scalable Spatial Data Analysis Using Markov Logic Networks (IS, MM, MFM), pp. 1834–1837.
VLDB-2019-KuhringI: I Can't Believe It's Not (Only) Software! Bionic Distributed Storage for Parquet Files (LK, ZI), pp. 1838–1841.
VLDB-2019-ChoiZBM: VISE: Vehicle Image Search Engine with Traffic Camera (HC, EZ, AB, RJM), pp. 1842–1845.
VLDB-2019-GoldbergMNR: WiClean: A System for Fixing Wikipedia Interlinks Using Revision History Patterns (SG, TM, SN, KR), pp. 1846–1849.
VLDB-2019-RoyJPGKC: SparkCruise: Handsfree Computation Reuse in Spark (AR, AJ, HP, AG, SK, CC), pp. 1850–1853.
VLDB-2019-SandhaCANS: In-database Distributed Machine Learning: Demonstration using Teradata SQL Engine (SSS, WC, MAK, SN, MBS), pp. 1854–1857.
VLDB-2019-LiCPZLY: SHOAL: Large-scale Hierarchical Taxonomy via Graph-based Query Coalition in E-commerce (ZL, XC, XP, PZ, YL, GY), pp. 1858–1861.
VLDB-2019-XuWDZHH: DPSAaS: Multi-Dimensional Data Sharing and Analytics as Services under Local Differential Privacy (MX, TW0, BD, JZ, CH, ZH), pp. 1862–1865.
VLDB-2019-CaoXXBY: PriSTE: Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services (YC0, YX, LX0, LB, MY), pp. 1866–1869.
VLDB-2019-DeutchMM: Datalignment: Ontology Schema Alignment Through Datalog Containment (DD, EM, YM), pp. 1870–1873.
VLDB-2019-GeGMCJZ: IHCS: An Integrated Hybrid Cleaning System (CG, YG, XM, LC0, CSJ, ZZ), pp. 1874–1877.
VLDB-2019-CostaGC: CAPRIO: Graph-based Integration of Indoor and Outdoor Data for Path Discovery (CC, XG, PKC), pp. 1878–1881.
VLDB-2019-WuYTSB: HERMIT in Action: Succinct Secondary Indexing Mechanism via Correlation Exploration (YW, JY0, YT, RS, RB), pp. 1882–1885.
VLDB-2019-LoudetPB: DISPERS: Securing Highly Distributed Queries on Personal Data Management Systems (JL, ISP, LB), pp. 1886–1889.
VLDB-2019-AkhterFK: Stateful Functions as a Service in Action (AA, MF, AK), pp. 1890–1893.
VLDB-2019-OrdookhaniansLN: Demonstration of Krypton: Optimized CNN Inference for Occlusion-based Deep CNN Explanations (AO, XL, SN, AK), pp. 1894–1897.
VLDB-2019-MiaoLR: LensXPlain: Visualizing and Explaining Contributing Subsets for Aggregate Query Answers (ZM, AL, SR), pp. 1898–1901.
VLDB-2019-ZhangI: Juneau: Data Lake Management for Jupyter (YZ, ZGI), pp. 1902–1905.
VLDB-2019-HasaniGHTAKD: ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse (SH, FG, SH, ST, AA, NK, GD0), pp. 1906–1909.
VLDB-2019-EssertelTWDR: Flare & Lantern: Efficiently Swapping Horses Midstream (GME, RYT, FW, JMD, TR), pp. 1910–1913.
VLDB-2019-MartinsCCFD: Trinity: An Extensible Synthesis Framework for Data Science (RM, JC, YC, YF, ID), pp. 1914–1917.
VLDB-2019-HuangMBMHM: PSynDB: Accurate and Accessible Private Data Generation (ZH, RM, GB, GM, MH, AM), pp. 1918–1921.
VLDB-2019-ChandramouliXLK: FishStore: Fast Ingestion and Indexing of Raw Data (BC, DX0, YL, DK), pp. 1922–1925.
VLDB-2019-DiaoGMM: Spade: A Modular Framework for Analytical Exploration of RDF Graphs (YD, PG, IM, MM), pp. 1926–1929.
VLDB-2019-DsilvaMK: Making an RDBMS Data Scientist Friendly: Advanced In-database Interactive Analytics with Visualization Support (JVD, FDM, BK), pp. 1930–1933.
VLDB-2019-ZaoukSLSDS: UDAO: A Next-Generation Unified Data Analytics Optimizer (KZ, FS, CL, AS, YD, PJS), pp. 1934–1937.
VLDB-2019-JoTYWYLM: AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets (SJ, IT, WY, XW0, CY0, DL, NM), pp. 1938–1941.
VLDB-2019-WangNLKZKB: GRANO: Interactive Graph-based Root Cause Analysis for Cloud-Native Distributed Data Platform (HW, PN, JL, SK, GZ, SK, SBR), pp. 1942–1945.
VLDB-2019-FreyMRTV: Dietcoin: Hardening Bitcoin Transaction Verification Process For Mobile Devices (DF, MXM, PLR, FT, SV), pp. 1946–1949.
VLDB-2019-SinglaEAM: Raptor: Large Scale Analysis of Big Raster and Vector Data (SS, AE, RA, MFM), pp. 1950–1953.
VLDB-2019-RezigCSSTMOTE: Data Civilizer 2.0: A Holistic Framework for Data Preparation and Analytics (EKR, LC0, MS, GS, WT, SM, MO, NT0, AKE), pp. 1954–1957.
VLDB-2019-SpiegelbergK: Tuplex: Robust, Efficient Analytics When Python Rules (LFS, TK), pp. 1958–1961.
VLDB-2019-RenggliHKSWZ: Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization (CR, FAH, BK, KS, WW0, CZ), pp. 1962–1965.
VLDB-2019-XueranCLCD: PivotE: Revealing and Visualizing the Underlying Entity Structures for Exploration (HX, JC, JL, YC, XD0), pp. 1966–1969.
VLDB-2019-LuCHB: Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems (JL, YC, HH, SB), pp. 1970–1973.
VLDB-2019-MengHSH: TextCube: Automated Construction and Multidimensional Exploration (YM, JH, JS, JH0), pp. 1974–1977.
VLDB-2019-Amer-YahiaR: The Ever Evolving Online Labor Market: Overview, Challenges and Opportunities (SAY, SBR), pp. 1978–1981.
VLDB-2019-SabekM: Machine Learning Meets Big Spatial Data (IS, MFM), pp. 1982–1985.
VLDB-2019-NargesianZMPA: Data Lake Management: Challenges and Opportunities (FN, EZ, RJM, KQP, PCA), pp. 1986–1989.
VLDB-2019-LakshmananST: Combating Fake News: A Data Management and Mining Perspective (LVSL, MS, ST), pp. 1990–1993.
VLDB-2019-AnciauxBPPS: Personal Database Security and Trusted Execution Environments: A Tutorial at the Crossroads (NA, LB, PP, ISP, GS), pp. 1994–1997.
VLDB-2019-KesslerHF: SAP HANA goes private - From Privacy Research to Privacy Aware Enterprise Analytics (SK, JH, JCF), pp. 1998–2009.
VLDB-2019-DamasioCGMMSZ: Guided automated learning for query workload re-optimization (GD, VC, PG, PM, AM, JS, CZ), pp. 2010–2021.
VLDB-2019-ChattopadhyayDL: Procella: Unifying serving and analytical data at YouTube (BC, PD, WL, OT, AM, AM, PH, HG, DL, SM, RE, NM, HCL, XZ, TX, LP, FS, TB, NM, SA, VL, BE), pp. 2022–2034.
VLDB-2019-LuZWLZSYPD: A Lightweight and Efficient Temporal Database Management System in TDSQL (WL0, ZZ, XW, HL, ZZ, ZS, SY, AP, XD0), pp. 2035–2046.
VLDB-2019-SherkatFABDPKKL: Native Store Extension for SAP HANA (RS, CF, MA, RB, AD, AP, PK, NK, CL, SS, SI, SG, RS, CG, NB, YW, VK, SP, DG, RA, PG), pp. 2047–2058.
VLDB-2019-ZhanSWPLWCLPZC: AnalyticDB: Real-time OLAP Database System at Alibaba Cloud (CZ, MS, CW, XP, LL, SW, ZC, FL0, YP, FZ, CC), pp. 2059–2070.
VLDB-2019-SchultzAC: Tunable Consistency in MongoDB (WS, TA, AC), pp. 2071–2081.
VLDB-2019-CaoYCZLQ: TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial (SC, XY, CC, JZ0, XL, YQ0), pp. 2082–2093.
VLDB-2019-ZhuZYLZALZ: AliGraph: A Comprehensive Graph Neural Network Platform (RZ, KZ, HY, WL, CZ, BA, YL, JZ), pp. 2094–2105.
VLDB-2019-ChenWNC: Customizable and Scalable Fuzzy Join for Big Data (ZC, YW, VRN, SC), pp. 2106–2117.
VLDB-2019-LiZLG: QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning (GL0, XZ, SL, BG), pp. 2118–2130.
VLDB-2019-KandulaLCF: Experiences with Approximating Queries in Microsoft's Production Big-Data Clusters (SK, KL, SC, MF), pp. 2131–2142.
VLDB-2019-AntonopoulosBCD: Constant Time Recovery in Azure SQL Database (PA, PB, WC, CD, RTK, HK, PP, ALR, CSR, GMV), pp. 2143–2154.
VLDB-2019-HuangSZFCLFLGZ: Yugong: Geo-Distributed Data and Job Placement at Scale (YH, YS, ZZ, YF, JC, JL, HF, CL, TG, JZ), pp. 2155–2169.
VLDB-2019-TanGPYSDSAK: Choosing A Cloud DBMS: Architectures and Tradeoffs (JT, TG, MP, XY, MS, DJD, MS, AA, TK), pp. 2170–2182.
VLDB-2019-ZhangWTCCCGF: S3: A Scalable In-memory Skip-List Index for Key-Value Store (JZ, SW, ZT, GC0, ZC, WC, YG, XF), pp. 2183–2194.
VLDB-2019-MassonRL: DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees (CM, JER, HKL), pp. 2195–2205.
VLDB-2019-LongWDLHCL: A Distributed System for Large-scale n-gram Language Models at Tencent (QL, WW, JD, SL, WH, FC, SL), pp. 2206–2217.
VLDB-2019-DursunBCSG: A Morsel-Driven Query Execution Engine for Heterogeneous Multi-Cores (KD, CB, UÇ, GS, WG), pp. 2218–2229.
VLDB-2019-CaoTAJYLGSBSCWM: Smile: A System to Support Machine Learning on EEG Data at Scale (LC0, WT, SA, JJ, YY, XL, WG, AS, LB, JS, RC, MBW, SM, MS), pp. 2230–2241.
VLDB-2019-GreenGLLMPSSV: Updating Graph Databases with Cypher (AG, PG, LL, TL, VM, SP, MS, PS, HV), pp. 2242–2253.
VLDB-2019-Kamsky: Adapting TPC-C Benchmark to Measure Performance of Multi-Document Transactions in MongoDB (AK), pp. 2254–2262.
VLDB-2019-Li: Cloud native database systems at Alibaba: Opportunities and Challenges (FL0), pp. 2263–2272.
VLDB-2019-Boehm: In-Memory for the masses: Enabling cost-efficient deployments of in-memory data management platforms for business applications (AB0), pp. 2273–2274.
VLDB-2019-HubailABCLMW: Couchbase Analytics: NoETL for Scalable NoSQL Data Analysis (MAH, AA, MB, MJC0, DL, IM, TW), pp. 2275–2286.
VLDB-2019-Coyler: Performance in the spotlight (AC), pp. 2287–2289.
VLDB-2019-AbouziedABS: Integration of Large-Scale Data Processing Systems and Traditional Parallel Database Technology (AA, DJA, KBP, AS), pp. 2290–2299.
VLDB-2019-CooperNRSSBJPWY: PNUTS to Sherpa: Lessons from Yahoo!'s Cloud Database (BFC, PPSN, RR, US, AS, PB, HAJ, NP, DW, RY), pp. 2300–2307.
VLDB-2019-Tan: What I probably did right and what I think I could have done better (WCT), p. 2308.
VLDB-2019-Parameswaran: Enabling Data Science for the Majority (AP), pp. 2309–2322.
VLDB-2019-RekatsinasRVZP: Opportunities for Data Management Research in the Era of Horizontal AI/ML (TR, SR, MV, CZ, NP), pp. 2323–2324.
VLDB-2019-BarthelsMTAH: Strong consistency is not hard to get: Two-Phase Locking and Two-Phase Commit on Thousands of Cores (CB, IM0, KT, GA, TH), pp. 2325–2338.
VLDB-2019-WeiLL: Discovery and Ranking of Embedded Uniqueness Constraints (ZW, UL, SL), pp. 2339–2352.
VLDB-2019-ChuZYWP: Online Density Bursting Subgraph Detection from Temporal Graphs (LC, YZ, YY0, LW, JP), pp. 2353–2365.
VLDB-2019-HolandaMMR: Progressive Indexes: Indexing for Interactive Data Analysis (PH, SM, HM, MR), pp. 2366–2378.
VLDB-2019-HanaiSTLTC: Distributed Edge Partitioning for Trillion-edge Graphs (MH, TS, WJT, ESL, GT0, WC), pp. 2379–2392.
VLDB-2019-AthanassoulisBI: Optimal Column Layout for Hybrid Workloads (MA, KSB, SI), pp. 2393–2407.
VLDB-2019-SintosAY: Selecting Data to Clean for Fact Checking: Minimizing Uncertainty vs. Maximizing Surprise (SS, PKA, JY0), pp. 2408–2421.

Proceedings of the 45th International Conference on Very Large Data BasesVLDB-2019, 2019.

Contents (221 items)

Proceedings of the 45th International Conference on Very Large Data Bases
VLDB-2019, 2019.