BibSLEIGH — Proceedings of the 44th International Conference on Very Large Data Bases

Proceedings of the 44th International Conference on Very Large Data Bases
VLDB-2018, 2018.

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@proceedings{VLDB-2018,
	journal       = "{Proceedings of the VLDB Endowment}",
	title         = "{Proceedings of the 44th International Conference on Very Large Data Bases}",
	volume        = 11,
	year          = 2018,
}

Contents (207 items)

VLDB-2018-MenonPM17: Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last (PM, AP, TCM), pp. 1–13.
VLDB-2018-LiuZHWXZL17: ProbeSim: Scalable Single-Source and Top-k SimRank Computations on Dynamic Graphs (YL, BZ, XH, ZW, XX, KZ0, JL), pp. 14–26.
VLDB-2018-GuagliardoL17: A Formal Semantics of SQL Queries, Its Validation, and Applications (PG, LL), pp. 27–39.
VLDB-2018-KimMS17: Efficient Haar+ Synopsis Construction for the Maximum Absolute Error Measure (JK, JKM, KS), pp. 40–52.
VLDB-2018-TaoDS17: Approximate String Joins with Abbreviations (WT, DD, MS), pp. 53–65.
VLDB-2018-NguyenATTW17: Query-Driven On-The-Fly Knowledge Base Construction (DBN, AA, KT, MT, GW), pp. 66–79.
VLDB-2018-PoppeLRM17: GRETA: Graph-based Real-time Event Trend Aggregation (OP, CL, EAR, DM0), pp. 80–92.
VLDB-2018-GuoLST17: Parallel Personalized Pagerank on Dynamic Graphs (WG, YL, MS0, KLT), pp. 93–106.
VLDB-2018-ShaLHT17: Accelerating Dynamic Graph Analytics on GPUs (MS0, YL, BH, KLT), pp. 107–120.
VLDB-2018-AppuswamyAPIA17: Analyzing the Impact of System Architecture on the Scalability of OLTP Engines for High-Contention Workloads (RA, ACGA, DP, MI, AA), pp. 121–134.
VLDB-2018-JungHK17: Scalable Database Logging for Multicores (HJ, HH, SK), pp. 135–148.
VLDB-2018-BonifatiMT17: An Analytical Study of Large SPARQL Query Logs (AB, WM, TT), pp. 149–161.
VLDB-2018-WangQSZTG17: Approximately Counting Triangles in Large Graph Streams Including Edge Duplicates with a Fixed Memory Usage (PW, YQ, YS, XZ0, JT, XG), pp. 162–175.
VLDB-2018-QiaoZC17: Subgraph Matching: on Compression and Computation (MQ, HZ, HC), pp. 176–188.
VLDB-2018-SinghMEMPQST17: Synthesizing Entity Matching Rules by Examples (RS0, VVM, AKE, SM, PP, JAQR, ASL, NT0), pp. 189–202.
VLDB-2018-HeSLXXCC17: Stylus: A Strongly-Typed Store for Serving Massive RDF Data (LH, BS, YL, HX, YX, EC, LC), pp. 203–216.
VLDB-2018-IoannouG17: Holistic Query Evaluation over Information Extraction Pipelines (EI, MNG), pp. 217–229.
VLDB-2018-PsaropoulosLMA17: Interleaving with Coroutines: A Practical Approach for Robust Index Joins (GP, TL, NM, AA), pp. 230–242.
VLDB-2018-WenQZCL17: Efficient Structural Graph Clustering: An Index-Based Approach (DW, LQ, YZ0, LC, XL0), pp. 243–255.
VLDB-2018-VimercatiFJLPS17: An Authorization Model for Multi-Provider Queries (SDCdV, SF, SJ, GL, SP, PS), pp. 256–268.
VLDB-2018-RatnerBEFWR17: Snorkel: Rapid Training Data Creation with Weak Supervision (AR, SHB, HRE, JAF, SW0, CR), pp. 269–282.
VLDB-2018-LiDV17: VERIFAS: A Practical Verifier for Artifact Systems (YL0, AD, VV), pp. 283–296.
VLDB-2018-JiaKSMEA17: A Distributed Multi-GPU System for Fast Graph Processing (ZJ, YK, GMS, PSM, ME, AA), pp. 297–310.
VLDB-2018-BleifussKN17: Efficient Denial Constraint Discovery with Hydra (TB, SK0, FN), pp. 311–323.
VLDB-2018-AzimKA17: ReCache: Reactive Caching for Fast Analytics over Heterogeneous Data (TA, MK, AA), pp. 324–337.
VLDB-2018-YuanQLCZ17: Effective and Efficient Dynamic Graph Coloring (LY, LQ, XL0, LC, WZ0), pp. 338–351.
VLDB-2018-ZacharatouDASF17: GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons (ETZ, HD, AA, CTS, JF), pp. 352–365.
VLDB-2018-ShahKZ17: Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? (VS, AK0, XZ0), pp. 366–379.
VLDB-2018-LiuC17: Worker Recommendation for Crowdsourced Q&A Services: A Triple-Factor Aware Approach (ZL0, LC0), pp. 380–392.
VLDB-2018-GongZY17: Clustering Stream Data by Exploring the Evolution of Density Mountain (SG, YZ, GY0), pp. 393–405.
VLDB-2018-WangJP17: Query Fresh: Log Shipping on Steroids (TW0, RJ0, IP), pp. 406–419.
VLDB-2018-SahuMSLO17: The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing (SS, AM, SS, JL, MTÖ), pp. 420–431.
VLDB-2018-RamachandraPEHG17: Froid: Optimization of Imperative Programs in a Relational Database (KR0, KP, KVE, AH, CAGL, CC), pp. 432–444.
VLDB-2018-LiUYK17: An Experimental Study on Hub Labeling based Shortest Path Algorithms (YL, LHU, MLY, NMK), pp. 445–457.
VLDB-2018-MerrittGCM17: Concurrent Log-Structured Memory for Many-Core Key-Value Stores (AM, AG, YC0, DSM), pp. 458–471.
VLDB-2018-CeccarelloFPPV17: Clustering Uncertain Graphs (MC, CF, AP, GP, FV), pp. 472–484.
VLDB-2018-AbdelazizMOAK17: Lusail: A System for Querying Linked Data at Scale (IA, EM, MO, AA, PK), pp. 485–498.
VLDB-2018-HarmouchN17: Cardinality Estimation: An Experimental Survey (HH, FN), pp. 499–512.
VLDB-2018-ParkOL17: SQL Statement Logging for Making SQLite Truly Lite (JHP, GO, SWL0), pp. 513–525.
VLDB-2018-JohnsonNS: Towards Practical Differential Privacy for SQL Queries (NMJ, JPN, DS), pp. 526–539.
VLDB-2018-ShraerADCBKSCFQ: CloudKit: Structured Storage for Mobile Applications (AS, AA, BD, CC, DB, EK, ES, HC, JF, JQ, JR, MF, MM, NW, NFF, NS, OH, PS, RP, SD, SG, SL, SH, VK, VH, WLY, YT), pp. 540–552.
VLDB-2018-ArulrajLML: BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory (JA, JJL, UFM, PÅL), pp. 553–565.
VLDB-2018-HuangJWCYYLGC: FlexPS: Flexible Parallelism Control in Parameter Server Architecture (YH, TJ, YW, ZC, XY, FY, JL, YG, JC), pp. 566–579.
VLDB-2018-YaghmazadehWD: Automated Migration of Hierarchical Data to Relational Tables using Programming-by-Example (NY, XW0, ID), pp. 580–593.
VLDB-2018-LuoKLHCZ: TOAIN: A Throughput Optimizing Adaptive Index for Answering Dynamic kNN Queries on Road Networks (SL, BK, GL0, JH, RC, YZ), pp. 594–606.
VLDB-2018-LiZLWZ: Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads (TL, JZ, JL0, WW0, CZ), pp. 607–620.
VLDB-2018-QiTCZ: Theoretically Optimal and Empirically Efficient R-trees with Strong Parallelizability (JQ0, YT, YC, RZ0), pp. 621–634.
VLDB-2018-LinC: Domain-Aware Multi-Truth Discovery from Conflicting Sources (XL, LC0), pp. 635–647.
VLDB-2018-TianHMS: Contention-Aware Lock Scheduling for Transactional Databases (BT, JH, BM, GS), pp. 648–662.
VLDB-2018-PatelDZPZSMS: Quickstep: A Data Platform Based on the Scaling-Up Approach (JMP, HD, JZ, NP, ZZ, MS, HM, SS0), pp. 663–676.
VLDB-2018-KondylakisDZP: Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes (HK, ND, KZ, TP), pp. 677–690.
VLDB-2018-AmmarMSJ: Distributed Evaluation of Subgraph Queries Using Worst-case Optimal and Low-Memory Dataflows (KA, FM, SS, MJ), pp. 691–704.
VLDB-2018-LiXTW: Model-free Control for Distributed Stream Data Processing using Deep Reinforcement Learning (TL, ZX, JT0, YW), pp. 705–718.
VLDB-2018-PsallidasW: Smoke: Fine-grained Lineage at Interactive Speed (FP, EW0), pp. 719–732.
VLDB-2018-IdrisUVVL: Conjunctive Queries with Inequalities Under Updates (MI, MU, SV, HV, WL), pp. 733–745.
VLDB-2018-YinSLELFBSD: Bubble Execution: Resource-aware Reliable Analytics at Cloud Scale (ZY, JS, ML, JE, HL, MF, JAB, CAS, NRD), pp. 746–758.
VLDB-2018-KruseN: Efficient Discovery of Approximate Dependencies (SK0, FN), pp. 759–772.
VLDB-2018-WangMM: RC-Index: Diversifying Answers to Range Queries (YW, AM, GM), pp. 773–786.
VLDB-2018-DingCGJB: UlTraMan: A Unified Platform for Big Trajectory Data Management and Analytics (XD, LC0, YG, CSJ, HB), pp. 787–799.
VLDB-2018-JindalKRP: Selecting Subexpressions to Materialize at Datacenter Scale (AJ, KK, SR, HP), pp. 800–812.
VLDB-2018-NargesianZPM: Table Union Search on Open Data (FN, EZ, KQP, RJM), pp. 813–825.
VLDB-2018-ChenZLL: Scalable Training of Hierarchical Topic Models (JC0, JZ0, JL, SL), pp. 826–839.
VLDB-2018-CoskunGK: Indexed Fast Network Proximity Querying (MC, AG, MK), pp. 840–852.
VLDB-2018-Zheng0Y: Order Dispatch in Price-aware Ridesharing (LZ, LC0, JY), pp. 853–865.
VLDB-2018-MouratidisT: Exact Processing of Uncertain Top-k Queries in Multi-criteria Settings (KM, BT), pp. 866–879.
VLDB-2018-Berti-EquilleHN: Discovery of Genuine Functional Dependencies from Relational Data with Missing Values (LBÉ, HH, FN, NN, ST), pp. 880–892.
VLDB-2018-CaiX0JOZ: Effective Temporal Dependence Discovery in Time Series Data (QC, ZX, GC0, HVJ, BCO, MZ), pp. 893–905.
VLDB-2018-AroraSK0: HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces (AA0, SS, PK, AB0), pp. 906–919.
VLDB-2018-AhmadKMMHKSE: LA3: A Scalable Link- and Locality-Aware Linear Algebra-Based Graph Analytics System (MYA, OK, AM, AM, MH, MK, MS, TE), pp. 920–933.
VLDB-2018-ZhangDYLFS: Trajectory Simplification: An Experimental Study and Quality Analysis (DZ, MD, DY, YL, JF, HTS), pp. 934–946.
VLDB-2018-AntenucciC: Constraint-based Explanation and Repair of Filter-Based Transformations (DA, MJC), pp. 947–960.
VLDB-2018-WangFGHMOT: Scalable Semantic Querying of Text (XW, AF, BG, AYH, GAM, HO, WCT), pp. 961–974.
VLDB-2018-BellomariniSG: The Vadalog System: Datalog-based Reasoning for Knowledge Graphs (LB, ES, GG), pp. 975–987.
VLDB-2018-MedyaVRS: Noticeable Network Delay Minimization via Node Upgrades (SM, JV, SR, AKS), pp. 988–1001.
VLDB-2018-PalkarTNTPNSSPA: Evaluating End-to-End Optimization for Data Analytics Applications in Weld (SP, JJT, DN, PT, RP, PN, AS, MS, HP, SPA, SM, MZ), pp. 1002–1015.
VLDB-2018-MullerMK: Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses (MM, GM, OK), pp. 1016–1028.
VLDB-2018-HanHXTST: Efficient Algorithms for Adaptive Influence Maximization (KH, KH, XX, JT0, AS, XT), pp. 1029–1040.
VLDB-2018-BreslowJ: Morton Filters: Faster, Space-Efficient Cuckoo Filters via Biasing, Compression, and Decoupled Logical Sparsity (AB, NJ), pp. 1041–1055.
VLDB-2018-BiCLZ: An Optimal and Progressive Approach to Online Search of Top-K Influential Communities (FB, LC, XL0, WZ0), pp. 1056–1068.
VLDB-2018-MeisterMS: Errata for “Analysis of two existing and one new dynamic programming algorithm for the generation of optimal bushy join trees without cross products” (AM0, GM, GS), pp. 1069–1070.
VLDB-2018-ParkMGJPK: Data Synthesis based on Generative Adversarial Networks (NP, MM, KG, SJ, HP, YK), pp. 1071–1083.
VLDB-2018-LockardDSE: CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web (CL, XLD, PS, AE), pp. 1084–1096.
VLDB-2018-NaziDNC: Efficient Estimation of Inclusion Coefficient using HyperLogLog Sketches (AN, BD, VRN, SC), pp. 1097–1109.
VLDB-2018-FierABLF: Set Similarity Joins on MapReduce: An Experimental Survey (FF, NA, PB, UL, JCF), pp. 1110–1122.
VLDB-2018-DingDWCN: Plan Stitch: Harnessing the Best of Many Plans (BD, SD, WW0, SC, VRN), pp. 1123–1136.
VLDB-2018-WangDLXZCCOR: ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications (SW, TTAD, QL, ZX, MZ, QC, GC0, BCO, PR), pp. 1137–1150.
VLDB-2018-AmmarO: Experimental Analysis of Distributed Graph Systems (KA, MTÖ), pp. 1151–1164.
VLDB-2018-HeCGZNC: Transform-Data-by-Example (TDE): An Extensible Search Engine for Data Transformations (YH, XC, KG, YZ, VRN, SC), pp. 1165–1177.
VLDB-2018-OKeeffeSP: Frontier: Resilient Edge Processing for the Internet of Things (DO, TS, PRP), pp. 1178–1191.
VLDB-2018-HaynesMABCC: LightDB: A DBMS for Virtual Reality Video (BH, AM, AA, MB, LC, AC), pp. 1192–1205.
VLDB-2018-McKennaMHM: Optimizing error of high-dimensional statistical queries under differential privacy (RM, GM, MH, AM), pp. 1206–1219.
VLDB-2018-LiuZZWZ: MLBench: Benchmarking Machine Learning Services Against Human Experts (YL, HZ, LZ, WW0, CZ), pp. 1220–1232.
VLDB-2018-ChenLZLYW: Maximum Co-located Community Search in Large Scale Social Networks (LC, CL, RZ0, JL0, XY0, BW0), pp. 1233–1246.
VLDB-2018-Zalipynis: ChronosDB: Distributed, File Based, Geospatial Array DBMS (RARZ), pp. 1247–1261.
VLDB-2018-MackeZHP: Adaptive Sampling for Rapidly Matching Histograms (SM, YZ, SH, AGP), pp. 1262–1275.
VLDB-2018-AsudehNATZDS: Leveraging Similarity Joins for Signal Reconstruction (AA, AN, JA, ST, NZ0, GD0, DS), pp. 1276–1288.
VLDB-2018-YuXPSRD: Sundial: Harmonizing Concurrency Control and Caching in a Distributed OLTP Database Management System (XY, YX, AP, DS0, LR, SD), pp. 1289–1302.
VLDB-2018-MaiZPXSVCKMKDR: Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems (LM, KZ, RP, LX, SS, SV, PC, TK, SM, VK, SD, SR), pp. 1303–1316.
VLDB-2018-MahajanKSAKE: In-RDBMS Hardware Acceleration of Advanced Analytics (DM, JKK, JS, AA, AK0, HE), pp. 1317–1331.
VLDB-2018-KolchinskyS: Join Query Optimization Techniques for Complex Event Processing Applications (IK, AS), pp. 1332–1345.
VLDB-2018-KolchinskyS18a: Efficient Adaptive Detection of Complex Event Patterns (IK, AS), pp. 1346–1359.
VLDB-2018-WolfBMWSG: Robustness Metrics for Relational Query Execution Plans (FW, MB, NM, PRW, KUS, MG), pp. 1360–1372.
VLDB-2018-ZhengYZC: Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition (WZ, JXY, LZ0, HC), pp. 1373–1386.
VLDB-2018-RammelaereG: Explaining Repaired Data with CFDs (JR, FG), pp. 1387–1399.
VLDB-2018-DsilvaMK: AIDA - Abstraction for Advanced In-Database Analytics (JVD, FDM, BK), pp. 1400–1413.
VLDB-2018-AgrawalCCEIKKLM: RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! - (DA, SC, BCR, AKE, YI, ZK, SK0, JL, EM, MO, PP, JAQR, NT0, ST, AT), pp. 1414–1427.
VLDB-2018-ChengJC: An Experimental Evaluation of Task Assignment in Spatial Crowdsourcing (PC0, XJ, LC0), pp. 1428–1440.
VLDB-2018-KumarC: 2SCENT: An Efficient Algorithm to Enumerate All Simple Temporal Cycles (RK0, TC), pp. 1441–1453.
VLDB-2018-EbraheemTJOT: Distributed Representations of Tuples for Entity Resolution (ME, ST, SRJ, MO, NT0), pp. 1454–1467.
VLDB-2018-HasaniTAKD: Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse (SH, ST, AA, NK, GD0), pp. 1468–1481.
VLDB-2018-ChuMRCS: Axiomatic Foundations and Algorithms for Deciding Semantic Equivalences of SQL Queries (SC, BM, JR, AC, DS), pp. 1482–1495.
VLDB-2018-AlmutairiYSFSZ: HomeRun: Scalable Sparse-Spectrum Reconstruction of Aggregated Historical Data (FMA, FY, HAS, CF, NDS, VZ), pp. 1496–1508.
VLDB-2018-KuoCKHM: Differentially Private Hierarchical Count-of-Counts Histograms (YHK, CCC, DK, MH, AM), pp. 1509–1521.
VLDB-2018-ZhangZSMC: Efficient Document Analytics on Compressed Data: Method, Challenges, Algorithms, Insights (FZ0, JZ, XS, OM, WC), pp. 1522–1535.
VLDB-2018-MullerDG: You Say 'What', I Hear 'Where' and 'Why'? (Mis-)Interpreting SQL to Derive Fine-Grained Provenance (TM, BD, TG), pp. 1536–1549.
VLDB-2018-SchulzBS: An Eight-Dimensional Systematic Evaluation of Optimized Search Algorithms on Modern Processors (LCS, DB, GS), pp. 1550–1562.
VLDB-2018-TrummerBN: Vocalizing Large Time Series Efficiently (IT, MB, RN), pp. 1563–1575.
VLDB-2018-PalkarABZ: Filter Before You Parse: Faster Analytics on Raw Data with Sparser (SP, FA, PB, MZ), pp. 1576–1589.
VLDB-2018-AbbasKCV: Streaming Graph Partitioning: An Experimental Study (ZA, VK, PC, VV), pp. 1590–1603.
VLDB-2018-CaiGZACOTTW: Efficient Distributed Memory Management with RDMA and Caching (QC, WG, HZ0, DA, GC0, BCO, KLT, YMT, SW), pp. 1604–1617.
VLDB-2018-DidonaGWZ: Causal Consistency and Latency Optimality: Friend or Foe? (DD, RG, JW, WZ), pp. 1618–1632.
VLDB-2018-TongZZCYX: A Unified Approach to Route Planning for Shared Mobility (YT, YZ, ZZ, LC0, JY, KX0), pp. 1633–1646.
VLDB-2018-GanDTSB: Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries (EG, JD, KST, VS, PB), pp. 1647–1660.
VLDB-2018-PandeyKNK: How Good Are Modern Spatial Analytics Systems? (VP, AK, TN0, AK), pp. 1661–1673.
VLDB-2018-RongYBEBLB: Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science (KR, CEY, KJB, HE, PB, PL, GCB), pp. 1674–1687.
VLDB-2018-JensenPT: ModelarDB: Modular Model-Based Time Series Management with Spark and Cassandra (SKJ, TBP, CT0), pp. 1688–1701.
VLDB-2018-JonathanMHLN: Exploiting Coroutines to Attack the “Killer Nanoseconds” (CJ, UFM, JH, JJL, GVN), pp. 1702–1714.
VLDB-2018-BindschaedlerGC: The Tao of Inference in Privacy-Protected Databases (VB, PG, DC, TR, VS), pp. 1715–1728.
VLDB-2018-DemertzisPT: Efficient Searchable Encryption Through Compression (ID, CP, RT), pp. 1729–1741.
VLDB-2018-LiGMDMW: Challenges and Experiences in Building an Efficient Apache Beam Runner For IBM Streams (SL, PG, JM, DD, WM, KLW), pp. 1742–1754.
VLDB-2018-BoehmRHSEP: On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML (MB0, BR, DH, PS, AVE, NP), pp. 1755–1768.
VLDB-2018-RehrmannBBKLR: OLTPShare: The Case for Sharing in OLTP Workloads (RR, CB, AB0, KK, WL, AR), pp. 1769–1780.
VLDB-2018-SchelterLSCBG: Automating Large-Scale Data Quality Verification (SS, DL, PS, MC, FB, AG), pp. 1781–1794.
VLDB-2018-ShachamGBBHK: Taking Omid to the Clouds: Fast, Scalable Transactions for Real-Time Cloud Analytics (OS, YG, AB, EB, EH, IK), pp. 1795–1808.
VLDB-2018-Jacques-SilvaLC: Providing Streaming Joins as a Service at Facebook (GJS, RL, LC, GJC, KC, TH, YM, KW, RS, SY, AB, BH, SI, AJ), pp. 1809–1821.
VLDB-2018-CaiCCCCDDDGHJLL: FusionInsight LibrA: Huawei's Enterprise Cloud Data Analytics Platform (LC, JC0, JC, YC, KC, MAD, YD, YD, AG, JH, KJ, SL, YL, DN, CP, JS, LZ, MZ0, CZ), pp. 1822–1834.
VLDB-2018-SamwelCHGVYPSTA: F1 Query: Declarative Querying at Scale (BS, JC, BH, JG, PV, CY, KP, JS, DT, HA, FW, DW, JY, JX, JL, ZY, CC, QZ, IR, AB, AH, YX, AG, AEH, OE, ZY, MY, YW, TD, CZ, GG, SS, AMA, DA, AG, SV), pp. 1835–1848.
VLDB-2018-CaoLWCZZWM: PolarFS: An Ultra-low Latency and Failure Resilient Distributed File System for Shared Storage Cloud Database (WC, ZL, PW, SC, CZ, SZ, YW, GM), pp. 1849–1862.
VLDB-2018-BortnikovBHKS: Accordion: Better Memory Organization for LSM Key-Value Stores (EB, AB, EH, IK, GS), pp. 1863–1875.
VLDB-2018-QiuCQPZLZ: Real-time Constrained Cycle Detection in Large Dynamic Graphs (XQ, WC, ZQ, YP, YZ0, XL0, JZ), pp. 1876–1888.
VLDB-2018-GurajadaGZPM: BTrim - Hybrid In-Memory Database Architecture for Extreme Transaction Processing in VLDBs (AG, DG, FZ, AP, ZFM), pp. 1889–1901.
VLDB-2018-SBHMO: Sherlock: A System for Interactive Summarization of Large Text Collections (APVS, CB, BH, CMM, OÖ), pp. 1902–1905.
VLDB-2018-BehrensCCGGL: DataStorm-FE: A Data- and Decision-Flow and Coordination Engine for Coupled Simulation Ensembles (HB, KSC, XC, AG, YG, MLL), pp. 1906–1909.
VLDB-2018-ZhangAWDJLSPG: A Demonstration of the OtterTune Automatic Database Management System Tuning Service (BZ, DVA, JW, TD, SJ, JL, SS, AP, GJG), pp. 1910–1913.
VLDB-2018-KakoulliKH: OctopusFS in Action: Tiered Storage Management for Data Intensive Computing (EK, NK, HH), pp. 1914–1917.
VLDB-2018-LiLSCCS: TRIPS: A System for Translating Raw Indoor Positioning Data into Visual Mobility Semantics (HL0, HL0, FS, GC0, KC0, LS), pp. 1918–1921.
VLDB-2018-KeTKY: A Demonstration of PERC: Probabilistic Entity Resolution With Crowd Errors (XK, MT, AK, VKY), pp. 1922–1925.
VLDB-2018-LiCFWLZLYZY: CDB: A Crowd-Powered Database System (GL0, CC, JF, XW, JL0, YZ, YL, XY, XZ, HY), pp. 1926–1929.
VLDB-2018-ChandramouliPKL: FASTER: An Embedded Concurrent Key-Value Store for State Management (BC, GP, DK, JJL, JH, MB), pp. 1930–1933.
VLDB-2018-ZhangL: Maverick: A System for Discovering Exceptional Facts from Knowledge Graphs (GZ, CL), pp. 1934–1937.
VLDB-2018-ChenGLXJZ: PTRider: A Price-and-Time-Aware Ridesharing System (LC0, YG, ZL, XX, CSJ, YZ), pp. 1938–1941.
VLDB-2018-BeheshtiBNT: CoreKG: a Knowledge Lake Service (AB, BB, RN, AT), pp. 1942–1945.
VLDB-2018-OrtonaMP: RuDiK: Rule Discovery in Knowledge Bases (SO, VVM, PP), pp. 1946–1949.
VLDB-2018-PapadakisTTGPK: The return of JedAI: End-to-End Entity Resolution for Structured and Semi-Structured Data (GP0, LT, ET, GG, TP, MK), pp. 1950–1953.
VLDB-2018-LeeLG: Provenance Summaries for Answers and Non-Answers (SL, BL, BG), pp. 1954–1957.
VLDB-2018-XinMLMSP: Helix: Accelerating Human-in-the-loop Machine Learning (DX, LM, JL, SM, SS, AGP), pp. 1958–1961.
VLDB-2018-SiddiquiLWKP: ShapeSearch: Flexible Pattern-based Querying of Trend Line Visualizations (TS, PL, ZW, KK, AGP), pp. 1962–1965.
VLDB-2018-XieBSCH: PANDA: A System for Partial Topology-based Search on Large Networks (MX, SSB, HS, GC, WSH), pp. 1966–1969.
VLDB-2018-LuZSPZDHWPL: MSQL+: a Plugin Toolkit for Similarity Search under Metric Spaces in Distributed Relational Database Systems (WL0, XZ, ZS, ZP, XZ0, XD0, HH, XW, AP, HL), pp. 1970–1973.
VLDB-2018-SanghiSSHT: HYDRA: A Dynamic Big Data Regenerator (AS, RS, DS, JRH, ST), pp. 1974–1977.
VLDB-2018-JamourAK: A Demonstration of MAGiQ: Matrix Algebra Approach for Solving RDF Graph Queries (FTJ, IA, PK), pp. 1978–1981.
VLDB-2018-TanZES: REGAL+: Reverse Engineering SPJA Queries (WCT, MZ, HE, DS), pp. 1982–1985.
VLDB-2018-DeutchFGH: NLProveNAns: Natural Language Provenance for Non-Answers (DD, NF, AG, TH), pp. 1986–1989.
VLDB-2018-XuLSM: Fault-Tolerance for Distributed Iterative Dataflows in Action (CX0, RPL, JS0, VM), pp. 1990–1993.
VLDB-2018-AbramovitzDG: QuestPro: Queries in SPARQL Through Provenance (EA, DD, AG), pp. 1994–1997.
VLDB-2018-JarovskyMNT: GOLDRUSH: Rule Sharing System for Fraud Detection (AJ, TM, SN, WCT), pp. 1998–2001.
VLDB-2018-AebeloeMSH: Discovering Diversified Paths in Knowledge Bases (CA, GM, VS, KH), pp. 2002–2005.
VLDB-2018-JunghannsKTGPR: Declarative and distributed graph analytics with GRADOOP (MJ, MK, NT, KG, AP, ER), pp. 2006–2009.
VLDB-2018-ZhangWT: A collaborative framework for tweaking properties in a synthetic dataset (JZ, YW, YCT), pp. 2010–2013.
VLDB-2018-JammiSMVPALKSS: Tooling Framework for Instantiating Natural Language Querying System (MJ, JS, ARM, SV, VP, RA, PL, HK, DS, KS), pp. 2014–2017.
VLDB-2018-WangKSFGHT: Koko: A System for Scalable Semantic Querying of Text (XW, JK, YS, AF, BG, AYH, WCT), pp. 2018–2021.
VLDB-2018-WangLMNT: GC: A Graph Caching System for Subgraph/Supergraph Queries (JW0, ZL, SM0, NN, PT), pp. 2022–2025.
VLDB-2018-LissandriniMPV: X2Q: Your Personal Example-based Graph Explorer (ML, DM, TP, YV), pp. 2026–2029.
VLDB-2018-ChanialDGLNM: ConnectionLens: Finding Connections Across Heterogeneous Data Sources (CC, RD, HG, JL, MHLN, IM), pp. 2030–2033.
VLDB-2018-SenellartJMR: ProvSQL: Provenance and Probability Management in PostgreSQL (PS, LJ, SM, YR), pp. 2034–2037.
VLDB-2018-ShangBEF: CYADB: A Database that Covers Your Ask (ZS, WB, AJE, MJF), pp. 2038–2041.
VLDB-2018-GovindPNCDPFCCS: CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching (YG, EP, PN, PSGC, AD, YP, GF, DC, MC, MS), pp. 2042–2045.
VLDB-2018-GrulichN: Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing (PMG, FN), pp. 2046–2049.
VLDB-2018-AgrawalF: Dhalion in Action: Automatic Management of Streaming Applications (AA, AF), pp. 2050–2053.
VLDB-2018-KarlasLWZ: Ease.ml in Action: Towards Multi-tenant Declarative Learning Services (BK, JL0, WW0, CZ), pp. 2054–2057.
VLDB-2018-NetoNSC: MustaCHE: A Multiple Clustering Hierarchies Explorer (ACAN, MAN, JS0, RJGBC), pp. 2058–2061.
VLDB-2018-SalimiCLGS: HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries (BS, CC, PL, JG, DS), pp. 2062–2065.
VLDB-2018-PicadoTP: Learning Efficiently Over Heterogeneous Databases (JP, AT, SP), pp. 2066–2069.
VLDB-2018-SantosARGM: Scalable and Efficient Data Analytics and Mining with Lemonade (WS, GdPA, MHR, DOG, WMJ), pp. 2070–2073.
VLDB-2018-TrummerMMJA: SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning (IT, SM, DM, SJ, JA), pp. 2074–2077.
VLDB-2018-VoLKW: iSPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data (HV, YL, JK, FW0), pp. 2078–2081.
VLDB-2018-SousaOMV: DfAnalyzer: Runtime Dataflow Analysis of Scientific Applications using Provenance (VSS, DdO0, MM, PV), pp. 2082–2085.
VLDB-2018-HynesDYCS: A Demonstration of Sterling: A Privacy-Preserving Data Marketplace (NH, DD, DY, RC0, DS), pp. 2086–2089.
VLDB-2018-CaoXYXZ: ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release (YC0, LX0, MY, YX, SZ), pp. 2090–2093.
VLDB-2018-DongR: Data Integration and Machine Learning: A Natural Synergy (LD, TR), pp. 2094–2097.
VLDB-2018-MaiyyaZAA: Database and Distributed Computing Fundamentals for Scalable, Fault-tolerant, and Consistent Maintenance of Blockchains (SM, VZ, DA, AEA), pp. 2098–2101.
VLDB-2018-FaloutsosGJW: Forecasting Big Time Series: Old and New (CF, JG, TJ, YW), pp. 2102–2105.
VLDB-2018-DeutschP: Graph Data Models, Query Languages and Programming Paradigms (AD, YP), pp. 2106–2109.
VLDB-2018-CazalensLMLT: Computational fact-checking: a content management perspective (SC, JL, IM, PL, XT), pp. 2110–2113.
VLDB-2018-FurtadoZ: Information and Data Management at PUC-Rio and UFMG (ALF, NZ), pp. 2114–2129.
VLDB-2018-Miller: Open Data Integration (RJM), pp. 2130–2139.
VLDB-2018-CafarellaHLMYWW: Ten Years of WebTables (MJC, AYH, HL, JM, CY0, DZW, EW0), pp. 2140–2149.
VLDB-2018-Kraska: Northstar: An Interactive Data Science System (TK), pp. 2150–2164.
VLDB-2018-StoyanovichHJM: Panel: A Debate on Data and Algorithmic Ethics (JS, BH, HVJ, GM), pp. 2165–2167.
VLDB-2018-ThomasK: A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics (AT, AK), pp. 2168–2182.
VLDB-2018-VenkateshHKP: A Concave Path to Low-overhead Robust Query Processing (SKV, JRH, SK, VP), pp. 2183–2195.
VLDB-2018-WenZRY: Interactive Summarization and Exploration of Top Aggregate Query Answers (YW, XZ, SR, JY0), pp. 2196–2208.
VLDB-2018-KerstenLKNPB: Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask (TK, VL, AK, TN0, AP, PAB), pp. 2209–2222.
VLDB-2018-GaoAY: Durable Top-k Queries on Temporal Data (JG, PKA, JY0), pp. 2223–2235.
VLDB-2018-LinardiP: Scalable, Variable-Length Similarity Search in Data Series: The ULISSE Approach (ML, TP), pp. 2236–2248.
VLDB-2018-SauerGH: FineLine: log-structured transactional storage and recovery (CS, GG, TH), pp. 2249–2262.
VLDB-2018-RahmanHN: ICARUS: Minimizing Human Effort in Iterative Data Completion (PR, CH, AN0), pp. 2263–2276.

Proceedings of the 44th International Conference on Very Large Data BasesVLDB-2018, 2018.

Contents (207 items)

Proceedings of the 44th International Conference on Very Large Data Bases
VLDB-2018, 2018.