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Tag #big data

201 papers:

ASPLOSASPLOS-2020-Kaplan
Big Data of the Past, from Venice to Europe (FK), p. 1.
ECSAECSA-2019-CastellanosPVVC #data analysis #deployment #overview
A Survey on Big Data Analytics Solutions Deployment (CC, BP, CAV, MDPV, DC), pp. 195–210.
ICSAICSA-2019-UllahB #adaptation #approach #architecture #security
An Architecture-Driven Adaptation Approach for Big Data Cyber Security Analytics (FU, MAB), pp. 41–50.
CIKMCIKM-2019-SalloumWH #approximate #clustering #data analysis
A Sampling-Based System for Approximate Big Data Analysis on Computing Clusters (SS, YW, JZH), pp. 2481–2484.
ICMLICML-2019-GhadikolaeiGFS #dataset #learning
Learning and Data Selection in Big Datasets (HSG, HGG, CF, MS), pp. 2191–2200.
KDDKDD-2019-Heckerman
Exploiting High Dimensionality in Big Data (DH), p. 3172.
PLDIPLDI-2019-WangCCZVML0X #hybrid #memory management #named
Panthera: holistic memory management for big data processing over hybrid memories (CW, HC, TC, JZ, HV0, OM, FL, XF0, GHX), pp. 347–362.
ESEC-FSEESEC-FSE-2019-BagherzadehK #developer #scalability #what
Going big: a large-scale study on what big data developers ask (MB, RK), pp. 432–442.
ESEC-FSEESEC-FSE-2019-GulzarMMK #data analysis #testing
White-box testing of big data analytics with complex user-defined functions (MAG, SM, MM, MK), pp. 290–301.
ASPLOSASPLOS-2019-GanZHCHPD #complexity #debugging #named #performance
Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices (YG0, YZ, KH, DC, YH, MP, CD), pp. 19–33.
ECSAECSA-2018-CastellanosCR #architecture #data analysis #modelling
Executing Architectural Models for Big Data Analytics (CC, DC, JDR), pp. 364–371.
EDMEDM-2018-SalesBPH #testing #using
Using Big Data to Sharpen Design-Based Inference in A/B Tests (AS, AB, TP, NTH).
CIKMCIKM-2018-BeretaCGKPSUVW
From Copernicus Big Data to Big Information and Big Knowledge: A Demo from the Copernicus App Lab Project (KB, HC, EG, MK, DAP, GS, SU, VV, FW), pp. 1911–1914.
CIKMCIKM-2018-BeretaKMSD
From Big Data to Big Information and Big Knowledge: the Case of Earth Observation Data (KB, MK, SM, GS, BD), pp. 2293–2294.
CIKMCIKM-2018-NidzwetzkiG #multi #scalability
BBoxDB - A Scalable Data Store for Multi-Dimensional Big Data (JKN, RHG), pp. 1867–1870.
KDDKDD-2018-Hodson #future of
Humans, Jobs, and the Economy: The Future of Finance in the Age of Big Data (JH), p. 2871.
KDDKDD-2018-LinKLZSXZQZ #identification #interactive #multi #named
BigIN4: Instant, Interactive Insight Identification for Multi-Dimensional Big Data (QL, WK, JGL, HZ0, KS, YX, ZZ, BQ, DZ), pp. 547–555.
KDDKDD-2018-NguyenLNPW #kernel #robust
Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data (KN, TL, TDN, DQP, GIW), pp. 2003–2011.
KDDKDD-2018-RongXYM #named #realtime
Du-Parking: Spatio-Temporal Big Data Tells You Realtime Parking Availability (YR, ZX, RY, XM), pp. 646–654.
KDDKDD-2018-Teh #learning #on the #problem
On Big Data Learning for Small Data Problems (YWT), p. 3.
ESEC-FSEESEC-FSE-2018-GulzarWK #automation #data analysis #data-driven #debugging #named #scalability
BigSift: automated debugging of big data analytics in data-intensive scalable computing (MAG, SW, MK), pp. 863–866.
ASPLOSASPLOS-2018-NguyenFNXDL #distributed #named
Skyway: Connecting Managed Heaps in Distributed Big Data Systems (KN, LF, CN, G(X, BD, SL), pp. 56–69.
CASECASE-2018-LongoFMMS #approach #complexity #monitoring
Big Data for advanced monitoring system: an approach to manage system complexity (CSL, CF, FM, LM, MS), pp. 341–346.
CoGCIG-2017-BertensGP #game studies #multi #predict #scalability
Games and big data: A scalable multi-dimensional churn prediction model (PB, AG, AP), pp. 33–36.
CIKMCIKM-2017-WangDCLG #named
CleanCloud: Cleaning Big Data on Cloud (HW, XD, XC, JL, HG), pp. 2543–2546.
KDDKDD-2017-Berglund #mining
Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy (AB), p. 11.
KDDKDD-2017-Cohen0Y #set
A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection (RC, LK0, AY), pp. 95–103.
KDDKDD-2017-KarpatneK #challenge #machine learning
Big Data in Climate: Opportunities and Challenges for Machine Learning (AK, VK), pp. 21–22.
KDDKDD-2017-Mazumdar #challenge #metric
Addressing Challenges with Big Data for Media Measurement (MM), p. 23.
KDDKDD-2017-YanCKR #detection #distributed
Distributed Local Outlier Detection in Big Data (YY, LC, CK, EAR), pp. 1225–1234.
ESEC-FSEESEC-FSE-2017-GarbervetskyP0M #optimisation #query #static analysis
Static analysis for optimizing big data queries (DG, ZP, MB0, MM, TM, EZ), pp. 932–937.
ASPLOSASPLOS-2017-Zhou #data analysis
Big Data Analytics and Intelligence at Alibaba Cloud (JZ), p. 1.
CASECASE-2017-ChenWWWC #classification #detection #fault #multi
Big data analytic for multivariate fault detection and classification in semiconductor manufacturing (YJC, BCW, JZW, YCW, CFC), pp. 731–736.
CASECASE-2017-ChenXZX #health #personalisation #quality
Big data analytic based personalized air quality health advisory model (LC, JX, LZ, YX), pp. 88–93.
CASECASE-2017-JiaSYCYG #data analysis #detection #multi
Big-data analysis of multi-source logs for anomaly detection on network-based system (ZJ, CS0, XY, YC, TY, XG), pp. 1136–1141.
CASECASE-2017-JongRANO #automation #estimation #problem #scheduling #towards
Big data in automation: Towards generalized makespan estimation in shop scheduling problems (AWdJ, JIUR, MA, TN, JO), pp. 1516–1521.
CASECASE-2017-WangOLC #problem #self #using
Capacitated competitive facility location problem of self-collection lockers by using public big data (YW0, TO, LHL, EPC), p. 1344.
CASECASE-2017-XianWL #adaptation #data type #monitoring #online #parametricity
A nonparametric adaptive sampling strategy for online monitoring of big data streams (XX, AW, KL), pp. 844–846.
WICSAWICSA-2016-KleinGAGGKNS #modelling
Model-Driven Observability for Big Data Storage (JK, IG, LA, JG, CG, RK, PN, VS), pp. 134–139.
CIKMCIKM-2016-ShiTWA #data mining #mining #visual notation
ACM DAVA'16: 2nd International Workshop on DAta mining meets Visual Analytics at Big Data Era (LS, HT, CW, LA), p. 2509.
CIKMCIKM-2016-ZhangLDCKS #locality #privacy #scalability #using
Scalable Local-Recoding Anonymization using Locality Sensitive Hashing for Big Data Privacy Preservation (XZ, CL, WD, JC, KR, ZS), pp. 1793–1802.
CIKMCIKM-2016-ZhengWPYFX #predict #using
Urban Traffic Prediction through the Second Use of Inexpensive Big Data from Buildings (ZZ, DW0, JP, YY, CF, LFX), pp. 1363–1372.
CIKMCIKM-2016-ZhuLYZZGDRZ #locality
City-Scale Localization with Telco Big Data (FZ, CL, MY, YZ, ZZ, TG, KD, WR, JZ), pp. 439–448.
ICPRICPR-2016-JoyR0V #optimisation #using
Hyperparameter tuning for big data using Bayesian optimisation (TTJ, SR, SG0, SV), pp. 2574–2579.
KDDKDD-2016-LiMLFDYLQ #data analysis #learning #performance #scalability #taxonomy
Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis (XL0, MM, BL, MSF, ID, JY, TL, SQ), pp. 511–519.
KDDKDD-2016-LiQJTYW #optimisation #parallel
Parallel Lasso Screening for Big Data Optimization (QL, SQ, SJ, PMT, JY, JW0), pp. 1705–1714.
KDDKDD-2016-MoralesBKGF #data type #mining
IoT Big Data Stream Mining (GDFM, AB, LK, JG, WF0), pp. 2119–2120.
KDDKDD-2016-SimoudisGGOS #lessons learnt
Big Data Needs Big Dreamers: Lessons from Successful Big Data Investors (ES, MG, TG, MO, GS), pp. 11–12.
KDDKDD-2016-WangKGL
Crime Rate Inference with Big Data (HW0, DK, CG, ZL), pp. 635–644.
ASEASE-2016-LiLKLG #combinator #generative #testing
Applying combinatorial test data generation to big data applications (NL, YL, HRK, JL, YG), pp. 637–647.
FSEFSE-2016-GulzarICK #data analysis #debugging #interactive #named
BigDebug: interactive debugger for big data analytics in Apache Spark (MAG, MI, TC, MK), pp. 1033–1037.
ICSE-2016-GulzarIYTCMK #debugging #interactive #named
BigDebug: debugging primitives for interactive big data processing in spark (MAG, MI, SY, SDT, TC, TDM, MK), pp. 784–795.
HTHT-2015-Smyth
From Small Sensors to Big Data (BS), p. 101.
JCDLJCDL-2015-KananZMF #learning #problem #summary
Big Data Text Summarization for Events: A Problem Based Learning Course (TK, XZ, MM, EAF), pp. 87–90.
JCDLJCDL-2015-XieCSWTK #data transformation #reuse #towards
Towards Use And Reuse Driven Big Data Management (ZX, YC, JS, TW, PAT, MK), pp. 65–74.
PODSPODS-2015-FanGCDL #query
Querying Big Data by Accessing Small Data (WF, FG, YC, TD, PL), pp. 173–184.
PODSPODS-2015-Jordan
Computational Thinking, Inferential Thinking and “Big Data” (MIJ), p. 1.
SIGMODSIGMOD-2015-CSKZYRPAKDRD #industrial #what #why
Why Big Data Industrial Systems Need Rules and What We Can Do About It (PSGC, CS, KGK, HZ, FY, NR, SP, EA, GK, RD, VR, AD), pp. 265–276.
SIGMODSIGMOD-2015-DokaPTMK #data analysis #multi #named #workflow
IReS: Intelligent, Multi-Engine Resource Scheduler for Big Data Analytics Workflows (KD, NP, DT, CM, NK), pp. 1451–1456.
SIGMODSIGMOD-2015-ElgamalYAMH #analysis #component #distributed #named #platform #scalability
sPCA: Scalable Principal Component Analysis for Big Data on Distributed Platforms (TE, MY, AA, WM, MH), pp. 79–91.
SIGMODSIGMOD-2015-HuangZYDLND0Z #predict
Telco Churn Prediction with Big Data (YH, FZ, MY, KD, YL, BN, WD, QY, JZ), pp. 607–618.
SIGMODSIGMOD-2015-KhayyatIJMOPQ0Y #named
BigDansing: A System for Big Data Cleansing (ZK, IFI, AJ, SM, MO, PP, JAQR, NT, SY), pp. 1215–1230.
SIGMODSIGMOD-2015-RablDFSJ
Just can’t get enough: Synthesizing Big Data (TR, MD, MF, SS, HAJ), pp. 1457–1462.
SIGMODSIGMOD-2015-YuanWYC #database #scalability
A Demonstration of Rubato DB: A Highly Scalable NewSQL Database System for OLTP and Big Data Applications (LYY, LW, JHY, YC), pp. 907–912.
SIGMODSIGMOD-2015-ZengADAS #analysis #interactive #named #online
G-OLA: Generalized On-Line Aggregation for Interactive Analysis on Big Data (KZ, SA, AD, MA, IS), pp. 913–918.
VLDBVLDB-2015-Balazinska15a #data analysis #industrial #problem #question
Big Data Research: Will Industry Solve all the Problems? (MB), pp. 2053–2064.
VLDBVLDB-2015-HuYYDCYGZ #difference #framework #platform #privacy
Differential Privacy in Telco Big Data Platform (XH, MY, JY, YD, LC, QY, HG, JZ), pp. 1692–1703.
VLDBVLDB-2015-LoghinTZOT #performance
A Performance Study of Big Data on Small Nodes (DL, BMT, HZ, BCO, YMT), pp. 762–773.
VLDBVLDB-2015-SH #approach #named #testing
CODD: A Dataless Approach to Big Data Testing (AS, JRH), pp. 2008–2019.
VLDBVLDB-2015-Walter
Big Plateaus of Big Data on the Big Island (TW), pp. 2057–2068.
EDMEDM-2015-MacHardyP #education #using
Evaluating Educational Videos using Bayesian Knowledge Tracing and Big Data (ZM, ZAP), pp. 424–427.
ICALPICALP-v1-2015-Canonne #testing
Big Data on the Rise? — Testing Monotonicity of Distributions (CLC), pp. 294–305.
ICALPICALP-v1-2015-Nikolov #database
An Improved Private Mechanism for Small Databases (AN), pp. 1010–1021.
HCIDUXU-DD-2015-FanHV #risk management
Supply Chain Risk Management in the Era of Big Data (YF, LH, SV), pp. 283–294.
HCIHIMI-IKD-2015-TrevisanPMG #health #industrial #problem #security #visualisation
Big Data Visualization for Occupational Health and Security Problem in Oil and Gas Industry (DGT, NSP, LM, ACBG), pp. 46–54.
ICEISICEIS-v1-2015-Aghbari #challenge #mining
Mining Big Data — Challenges and Opportunities (ZAA), pp. 379–384.
ICEISICEIS-v1-2015-AnjosFBSGM #search-based
Genetic Mapping of Diseases through Big Data Techniques (JCSdA, BRF, JFB, RBS, CG, UM), pp. 279–286.
ICMLICML-2015-EggelingKG
Dealing with small data: On the generalization of context trees (RE, MK, IG), pp. 1245–1253.
ICMLICML-2015-HoangHL #framework #modelling #probability #process
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data (TNH, QMH, BKHL), pp. 569–578.
KDDKDD-2015-BifetMRHP #classification #data type #evaluation #online #performance
Efficient Online Evaluation of Big Data Stream Classifiers (AB, GDFM, JR, GH, BP), pp. 59–68.
KDDKDD-2015-DhurandharGRME #risk management
Big Data System for Analyzing Risky Procurement Entities (AD, BG, RKR, GM, ME), pp. 1741–1750.
KDDKDD-2015-FeldmanT #approximate #constraints #matrix
More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data (DF, TT), pp. 249–258.
KDDKDD-2015-HsiehLZ #quality #recommendation
Inferring Air Quality for Station Location Recommendation Based on Urban Big Data (HPH, SDL, YZ), pp. 437–446.
KDDKDD-2015-John #case study #how
How Artificial Intelligence and Big Data Created Rocket Fuel: A Case Study (GJ), p. 1629.
KDDKDD-2015-XingHDKWLZXKY #distributed #framework #machine learning #named #platform
Petuum: A New Platform for Distributed Machine Learning on Big Data (EPX, QH, WD, JKK, JW, SL, XZ, PX, AK, YY), pp. 1335–1344.
KDDKDD-2015-YangLJ #data analysis #optimisation
Big Data Analytics: Optimization and Randomization (TY, QL, RJ), p. 2327.
KDDKDD-2015-ZhengYLLSCL #fine-grained #quality
Forecasting Fine-Grained Air Quality Based on Big Data (YZ, XY, ML, RL, ZS, EC, TL), pp. 2267–2276.
SKYSKY-2015-Allalouf #library #mining #visualisation
Big Data in the Library: Extending Modern Library Catalogues with Data Visualization, Linking and Mining (MA), pp. 74–75.
ICSEICSE-v2-2015-NagappanM #re-engineering
Big(ger) Data in Software Engineering (MN, MM), pp. 957–958.
ICSEICSE-v2-2015-ZhouLZLLQ #empirical #framework #platform #quality
An Empirical Study on Quality Issues of Production Big Data Platform (HZ, JGL, HZ, HL, HL, TQ), pp. 17–26.
SACSAC-2015-Rekha #detection #performance #using
A fast support vector data description system for anomaly detection using big data (AGR), pp. 931–932.
ASPLOSASPLOS-2015-Gidra0SSN #garbage collection #named
NumaGiC: a Garbage Collector for Big Data on Big NUMA Machines (LG, GT, JS, MS, NN), pp. 661–673.
ASPLOSASPLOS-2015-NguyenWBFHX #bound #compilation #named #runtime
FACADE: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications (KN, KW, YB, LF, JH, G(X), pp. 675–690.
CASECASE-2015-LeeC
Aggregate production planning with small data in TFT-LCD manufacturing (CYL, MCC), pp. 647–648.
DATEDATE-2015-DubenSPYAEPP #case study #energy #performance
Opportunities for energy efficient computing: a study of inexact general purpose processors for high-performance and big-data applications (PDD, JS, P, SY, JA, CCE, KVP, TNP), pp. 764–769.
DATEDATE-2015-KanounS #concept #data type #detection #learning #online #scheduling #streaming
Big-data streaming applications scheduling with online learning and concept drift detection (KK, MvdS), pp. 1547–1550.
DATEDATE-2015-ParkAHYL #energy #gpu #low cost #memory management #performance
Memory fast-forward: a low cost special function unit to enhance energy efficiency in GPU for big data processing (EP, JA, SH, SY, SL), pp. 1341–1346.
DATEDATE-2015-WangLZ #named
SODA: software defined FPGA based accelerators for big data (CW, XL, XZ), pp. 884–887.
ICSTICST-2015-LiEGO #framework #scalability
A Scalable Big Data Test Framework (NL, AE, YG, JO), pp. 1–2.
DocEngDocEng-2014-SchmitzP #tool support
Humanist-centric tools for big data: berkeley prosopography services (PS, LP), pp. 179–188.
HTHT-2014-Hidalgo #comprehension #development #network #social #visualisation
Big data visualization engines for understanding the development of countries, social networks, culture and cities (CAH), p. 3.
JCDLJCDL-2014-WuWKWCHTCOMG #challenge #framework #platform #towards
Towards building a scholarly big data platform: Challenges, lessons and opportunities (ZW, JW, MK, KW, HHC, WH, ST, SRC, AO, PM, CLG), pp. 117–126.
PODSPODS-2014-FanGL #independence #on the #query
On scale independence for querying big data (WF, FG, LL), pp. 51–62.
SIGMODSIGMOD-2014-HalperinACCKMORWWXBHS #data transformation
Demonstration of the Myria big data management service (DH, VTdA, LLC, SC, PK, DM, JO, VR, JW, AW, SX, MB, BH, DS), pp. 881–884.
SIGMODSIGMOD-2014-IstvanWA #data flow
Histograms as a side effect of data movement for big data (ZI, LW, GA), pp. 1567–1578.
SIGMODSIGMOD-2014-LeFevreSHTPC #data analysis #design #physics
Opportunistic physical design for big data analytics (JL, JS, HH, JT, NP, MJC), pp. 851–862.
SIGMODSIGMOD-2014-LeFevreSHTPC14a #multi #named #query
MISO: souping up big data query processing with a multistore system (JL, JS, HH, JT, NP, MJC), pp. 1591–1602.
SIGMODSIGMOD-2014-OzcanTAKMRW #question
Are we experiencing a big data bubble? (, NT, DJA, MK, CM, KR, JLW), pp. 1407–1408.
SIGMODSIGMOD-2014-SolimanAREGSCGRPWNKB #architecture #composition #named #query
Orca: a modular query optimizer architecture for big data (MAS, LA, VR, AEH, ZG, ES, GCC, CGA, FR, MP, FW, SN, KK, RB), pp. 337–348.
SIGMODSIGMOD-2014-ZoumpatianosIP #interactive
Indexing for interactive exploration of big data series (KZ, SI, TP), pp. 1555–1566.
VLDBVLDB-2014-CaoWR #data type #interactive
Interactive Outlier Exploration in Big Data Streams (LC, QW, EAR), pp. 1621–1624.
VLDBVLDB-2014-Jiang0OTW #named #scalability
epiC: an Extensible and Scalable System for Processing Big Data (DJ, GC, BCO, KLT, SW), pp. 541–552.
VLDBVLDB-2014-LeiZRE #framework #query
Redoop Infrastructure for Recurring Big Data Queries (CL, ZZ, EAR, MYE), pp. 1589–1592.
VLDBVLDB-2014-LiLZ #challenge #enterprise
Enterprise Search in the Big Data Era: Recent Developments and Open Challenges (YL, ZL, HZ), pp. 1717–1718.
VLDBVLDB-2014-Markl #data analysis #declarative #independence
Breaking the Chains: On Declarative Data Analysis and Data Independence in the Big Data Era (VM), pp. 1730–1733.
VLDBVLDB-2014-SimmenSDHLMSTX #graph #scalability
Large-Scale Graph Analytics in Aster 6: Bringing Context to Big Data Discovery (DES, KS, JD, YH, SL, AM, VS, MT, YX), pp. 1405–1416.
VLDBVLDB-2014-SuchanekW #data analysis #knowledge base
Knowledge Bases in the Age of Big Data Analytics (FMS, GW), pp. 1713–1714.
VLDBVLDB-2014-SuSGOS #java
Changing Engines in Midstream: A Java Stream Computational Model for Big Data Processing (XS, GS, BG, BO, PS), pp. 1343–1354.
VLDBVLDB-2014-WuC0SCB #named
yzBigData: Provisioning Customizable Solution for Big Data (SW, GC, KC, LS, HC, HB), pp. 1778–1783.
VLDBVLDB-2014-YuYWLC #classification #design #detection #power management
Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes (MCY, TY, SCW, CJL, EYC), pp. 1429–1440.
VLDBVLDB-2014-ZhangJSR #recommendation #using
Getting Your Big Data Priorities Straight: A Demonstration of Priority-based QoS using Social-network-driven Stock Recommendation (RZ, RJ, PS, LR), pp. 1665–1668.
VLDBVLDB-2015-GraefeVKKTLV14 #in memory #performance
In-Memory Performance for Big Data (GG, HV, HK, HAK, JT, ML, ACV), pp. 37–48.
EDMEDM-2014-YudelsonFRBNJ
Better Data Beats Big Data (MY, SF, SR, SRB, TN, AJ), pp. 205–208.
ICSMEICSME-2014-SvajlenkoIKRM #benchmark #metric #towards
Towards a Big Data Curated Benchmark of Inter-project Code Clones (JS, JFI, IK, CKR, MMM), pp. 476–480.
HCIDUXU-DI-2014-Bockermann #approach #data analysis #programming #visual notation
A Visual Programming Approach to Big Data Analytics (CB), pp. 393–404.
EDOCEDOC-2014-Ludwig #effectiveness #perspective
Managing Big Data Effectively — A Cloud Provider and a Cloud Consumer Perspective (HL), p. 91.
CIKMCIKM-2014-WangLBLGZ #named
Cleanix: A Big Data Cleaning Parfait (HW, ML, YB, JL, HG, JZ), pp. 2024–2026.
CIKMCIKM-2014-YuanWYC #database #grid #scalability #staged
Rubato DB: A Highly Scalable Staged Grid Database System for OLTP and Big Data Applications (LYY, LW, JHY, YC), pp. 1–10.
ECIRECIR-2014-CarageaWCWRCWG #dataset
CiteSeer x : A Scholarly Big Dataset (CC, JW, AMC, KW, JPFR, HHC, ZW, CLG), pp. 311–322.
ICMLICML-c2-2014-Chen0 #learning #modelling #topic #using
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (ZC, BL), pp. 703–711.
ICMLICML-c2-2014-DefazioDC #incremental #named #performance #problem
Finito: A faster, permutable incremental gradient method for big data problems (AD, JD, TSC), pp. 1125–1133.
KDDKDD-2014-AnagnostopoulosT #scalability
Scaling out big data missing value imputations: pythia vs. godzilla (CA, PT), pp. 651–660.
KDDKDD-2014-Chen0 #documentation #mining #topic
Mining topics in documents: standing on the shoulders of big data (ZC, BL), pp. 1116–1125.
KDDKDD-2014-CormodeD #tutorial
Sampling for big data: a tutorial (GC, NGD), p. 1975.
KDDKDD-2014-Eagle #social
Big data for social good (NE), p. 1522.
KDDKDD-2014-FengGBEHM #database #experience #in memory #query
Management and analytic of biomedical big data with cloud-based in-memory database and dynamic querying: a hands-on experience with real-world data (MF, MG, TB, JE, IH, RM), p. 1970.
KEODKEOD-2014-Bergamaschi #challenge #integration #state of the art
Big Data Integration — State of the Art & Challenges (SB), pp. 1–7.
KEODKEOD-2014-SurynekS #challenge #graph #information management #logic #perspective #reasoning
Theoretical Challenges in Knowledge Discovery in Big Data — A Logic Reasoning and a Graph Theoretical Point of View (PS, PS), pp. 327–332.
KEODKEOD-2014-Talia #data mining #distributed #information management #mining
Big Data Mining Services and Distributed Knowledge Discovery Applications on Clouds (DT), pp. 1–5.
KMISKMIS-2014-HeavinDA #information management
Small Data to Big Data — The Information Systems (IS) Continuum (CH, MD, FA), pp. 289–297.
SIGIRSIGIR-2014-Williams #how
The data revolution: how companies are transforming with big data (HEW), pp. 525–526.
ASEASE-2014-StephenSSE #program analysis
Program analysis for secure big data processing (JJS, SS, RS, PTE), pp. 277–288.
SACSAC-2014-EvermannA #algorithm #implementation #mining #process
Big data meets process mining: implementing the alpha algorithm with map-reduce (JE, GA), pp. 1414–1416.
DATEDATE-2014-0002LLCXY #data analysis #energy #network #performance
Energy efficient neural networks for big data analytics (YW, BL, RL, YC, NX, HY), pp. 1–2.
HPCAHPCA-2014-WangZLZYHGJSZZLZLQ #benchmark #internet #metric #named
BigDataBench: A big data benchmark suite from internet services (LW, JZ, CL, YZ, QY, YH, WG, ZJ, YS, SZ, CZ, GL, KZ, XL, BQ), pp. 488–499.
PDPPDP-2014-GrunzkeHSKGHHKPHMJ #case study #data transformation #metadata
Device-Driven Metadata Management Solutions for Scientific Big Data Use Cases (RG, JH, JS, NK, SG, MH, VH, SK, JP, MH, RMP, RJ), pp. 317–321.
ICLPICLP-J-2014-TachmazidisAF #performance #semantics
Efficient Computation of the Well-Founded Semantics over Big Data (IT, GA, WF), pp. 445–459.
SIGMODSIGMOD-2013-AboulnagaB #data analysis
Workload management for big data analytics (AA, SB), pp. 929–932.
SIGMODSIGMOD-2013-BarnettCDDFGMP #exclamation #interactive
Stat!: an interactive analytics environment for big data (MB, BC, RD, SMD, DF, JG, PM, JCP), pp. 1013–1016.
SIGMODSIGMOD-2013-CondieMPW #machine learning
Machine learning for big data (TC, PM, NP, MW), pp. 939–942.
SIGMODSIGMOD-2013-GhazalRHRPCJ #benchmark #data analysis #industrial #metric #named #standard #towards
BigBench: towards an industry standard benchmark for big data analytics (AG, TR, MH, FR, MP, AC, HAJ), pp. 1197–1208.
SIGMODSIGMOD-2013-MishneDLSL #architecture #performance #query #realtime #twitter
Fast data in the era of big data: Twitter’s real-time related query suggestion architecture (GM, JD, ZL, AS, JL), pp. 1147–1158.
SIGMODSIGMOD-2013-NazarukR
Big data in capital markets (AN, MR), pp. 917–918.
SIGMODSIGMOD-2013-SuchanekW
Knowledge harvesting in the big-data era (FMS, GW), pp. 933–938.
SIGMODSIGMOD-2013-SumbalyKS #ecosystem
The big data ecosystem at LinkedIn (RS, JK, SS), pp. 1125–1134.
VLDBVLDB-2013-BediniEV #case study #framework #platform #scalability
The Trento Big Data Platform for Public Administration and Large Companies: Use cases and Opportunities (IB, BE, YV), pp. 1166–1167.
VLDBVLDB-2013-BellamkondaLJZLC #adaptation #execution #parallel
Adaptive and Big Data Scale Parallel Execution in Oracle (SB, HGL, UJ, YZ, VL, TC), pp. 1102–1113.
VLDBVLDB-2013-ChandramouliGQ #in the cloud #scalability
Scalable Progressive Analytics on Big Data in the Cloud (BC, JG, AQ), pp. 1726–1737.
VLDBVLDB-2013-DongS #integration
Big Data Integration (XLD, DS), pp. 1188–1189.
VLDBVLDB-2013-FanGN #preprocessor #query
Making Queries Tractable on Big Data with Preprocessing (WF, FG, FN), pp. 685–696.
VLDBVLDB-2013-Franceschini #approach #how #open source
How to maximize the value of big data with the open source SpagoBI suite through a comprehensive approach (MF), pp. 1170–1171.
VLDBVLDB-2013-Hoppe #automation #learning #ontology #web
Automatic ontology-based User Profile Learning from heterogeneous Web Resources in a Big Data Context (AH), pp. 1428–1433.
VLDBVLDB-2013-SathiamoorthyAPDVCB #novel
XORing Elephants: Novel Erasure Codes for Big Data (MS, MA, DSP, AGD, RV, SC, DB), pp. 325–336.
VLDBVLDB-2013-TranBD #design #problem #query
Designing Query Optimizers for Big Data Problems of The Future (NT, SB, JD), pp. 1168–1169.
ICALPICALP-v2-2013-BachrachP #performance #pseudo #recommendation #sketching #using
Sketching for Big Data Recommender Systems Using Fast Pseudo-random Fingerprints (YB, EP), pp. 459–471.
HCIDUXU-WM-2013-LiuVMM #design #experience #framework #interactive #mining #platform #visualisation
Designing Discovery Experience for Big Data Interaction: A Case of Web-Based Knowledge Mining and Interactive Visualization Platform (QL, MV, KPCM, AFM), pp. 543–552.
CIKMCIKM-2013-Giles #data mining #information management #mining
Scholarly big data: information extraction and data mining (CLG), pp. 1–2.
KDDKDD-2013-CannyZ #data analysis
Big data analytics with small footprint: squaring the cloud (JC, HZ), pp. 95–103.
KDDKDD-2013-GetoorM
Entity resolution for big data (LG, AM), p. 1527.
KDDKDD-2013-Neumann #problem #using
Using “big data” to solve “small data” problems (CN), p. 1140.
KDDKDD-2013-RamanSGJ #pipes and filters
Beyond myopic inference in big data pipelines (KR, AS, JG, TJ), pp. 86–94.
KDDKDD-2013-SunR #data analysis
Big data analytics for healthcare (JS, CKR), p. 1525.
KDDKDD-2013-ZhengLH #named #quality
U-Air: when urban air quality inference meets big data (YZ, FL, HPH), pp. 1436–1444.
MLDMMLDM-2013-Suthaharan #classification #network
A Single-Domain, Representation-Learning Model for Big Data Classification of Network Intrusion (SS), pp. 296–310.
SEKESEKE-2013-Khoshgoftaar #challenge
Overcoming Big Data Challenges (TMK).
SIGIRSIGIR-2013-Smith #multi
Riding the multimedia big data wave (JRS), pp. 1–2.
ICSEICSE-2013-ShangJHAHM #data analysis #developer
Assisting developers of big data analytics applications when deploying on hadoop clouds (WS, ZMJ, HH, BA, AEH, PM), pp. 402–411.
HPDCHPDC-2013-XuS0 #named
IBIS: interposed big-data I/O scheduler (YX, AS, MZ), pp. 109–110.
ISMMISMM-2013-BuBXC #design
A bloat-aware design for big data applications (YB, VRB, G(X, MJC), pp. 119–130.
WICSA-ECSAWICSA-ECSA-2012-BegoliH #design #effectiveness #information management
Design Principles for Effective Knowledge Discovery from Big Data (EB, JLH), pp. 215–218.
PODSPODS-2012-Chaudhuri #data transformation #research #what
What next?: a half-dozen data management research goals for big data and the cloud (SC), pp. 1–4.
SIGMODSIGMOD-2012-ChengQR #data analysis #named
GLADE: big data analytics made easy (YC, CQ, FR), pp. 697–700.
VLDBVLDB-2012-AlsubaieeAABBBCGHKLOPVW #analysis #data transformation #named #open source
ASTERIX: An Open Source System for “Big Data” Management and Analysis (SA, YA, HA, AB, VRB, YB, MJC, RG, ZH, YSK, CL, NO, PP, RV, JW), pp. 1898–1901.
VLDBVLDB-2012-ChenAK #interactive #pipes and filters
Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads (YC, SA, RHK), pp. 1802–1813.
VLDBVLDB-2012-DittrichQ #performance #pipes and filters
Efficient Big Data Processing in Hadoop MapReduce (JD, JAQR), pp. 2014–2015.
VLDBVLDB-2012-LabrinidisJ #challenge
Challenges and Opportunities with Big Data (AL, HVJ), pp. 2032–2033.
VLDBVLDB-2012-RablSJGMM #challenge #enterprise #performance
Solving Big Data Challenges for Enterprise Application Performance Management (TR, MS, HAJ, SGV, VMM, SM), pp. 1724–1735.
VLDBVLDB-2012-Shim #algorithm #data analysis #pipes and filters
MapReduce Algorithms for Big Data Analysis (KS), pp. 2016–2017.
VLDBVLDB-2012-XuLGC #analysis #clustering #in the cloud #interactive #named #visual notation
CloudVista: Interactive and Economical Visual Cluster Analysis for Big Data in the Cloud (HX, ZL, SG, KC), pp. 1886–1889.
ICMLICML-2012-KleinerTSJ
The Big Data Bootstrap (AK, AT, PS, MIJ), p. 232.
KDDKDD-2012-Jordan #divide and conquer #statistics
Divide-and-conquer and statistical inference for big data (MIJ), p. 4.
KDDKDD-2012-Kitsuregawa
Building an engine for big data (MK), p. 223.
HPDCHPDC-2012-Budiu #artificial reality #framework #platform
Putting a “big-data” platform to good use: training kinect (MB), pp. 1–2.
VLDBVLDB-2011-Campbell #question
Is It Still “Big Data” If It Fits In My Pocket? (DC), p. 694.
SIGMODSIGMOD-2010-Amer-YahiaDKKF #algorithm
Crowds, clouds, and algorithms: exploring the human side of “big data” applications (SAY, AD, JMK, NK, MJF), pp. 1259–1260.
VLDBVLDB-2010-AgrawalDA #in the cloud #question
Big Data and Cloud Computing: New Wine or just New Bottles? (DA, SD, AEA), pp. 1647–1648.
FSEFSE-2010-Eagle #development #social
Big data, global development, and complex social systems (NE), pp. 3–4.
VLDBVLDB-2009-CohenDDHW #analysis
MAD Skills: New Analysis Practices for Big Data (JC, BD, MD, JMH, CW), pp. 1481–1492.
SACSAC-2000-AbiadHM #database #metric
Software Metrics for Small Database Applications (SA, RAH, NM), pp. 866–870.
CHICHI-1992-RiemanDR #database #lessons learnt #overview
A visit to a very small database: lessons from managing the review of papers submitted for CHI 1991 (JR, SD, JR), pp. 471–478.
SOSPSOSP-1987-BirrellJW #database #implementation #performance
A Simple and Efficient Implementation for Small Databases (AB, MBJ, EW), pp. 149–154.

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