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

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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