BibSLEIGH — big data tag

big data

big data

Tag #big data

201 papers:

ASPLOS-2020-Kaplan: Big Data of the Past, from Venice to Europe (FK), p. 1.
ECSA-2019-CastellanosPVVC #data analysis #deployment #overview: A Survey on Big Data Analytics Solutions Deployment (CC, BP, CAV, MDPV, DC), pp. 195–210.
ICSA-2019-UllahB #adaptation #approach #architecture #security: An Architecture-Driven Adaptation Approach for Big Data Cyber Security Analytics (FU, MAB), pp. 41–50.
CIKM-2019-SalloumWH #approximate #clustering #data analysis: A Sampling-Based System for Approximate Big Data Analysis on Computing Clusters (SS, YW, JZH), pp. 2481–2484.
ICML-2019-GhadikolaeiGFS #dataset #learning: Learning and Data Selection in Big Datasets (HSG, HGG, CF, MS), pp. 2191–2200.
KDD-2019-Heckerman: Exploiting High Dimensionality in Big Data (DH), p. 3172.
PLDI-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-FSE-2019-BagherzadehK #developer #scalability #what: Going big: a large-scale study on what big data developers ask (MB, RK), pp. 432–442.
ESEC-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.
ASPLOS-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.
ECSA-2018-CastellanosCR #architecture #data analysis #modelling: Executing Architectural Models for Big Data Analytics (CC, DC, JDR), pp. 364–371.
EDM-2018-SalesBPH #testing #using: Using Big Data to Sharpen Design-Based Inference in A/B Tests (AS, AB, TP, NTH).
CIKM-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.
CIKM-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.
CIKM-2018-NidzwetzkiG #multi #scalability: BBoxDB - A Scalable Data Store for Multi-Dimensional Big Data (JKN, RHG), pp. 1867–1870.
KDD-2018-Hodson #future of: Humans, Jobs, and the Economy: The Future of Finance in the Age of Big Data (JH), p. 2871.
KDD-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.
KDD-2018-NguyenLNPW #kernel #robust: Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data (KN, TL, TDN, DQP, GIW), pp. 2003–2011.
KDD-2018-RongXYM #named #realtime: Du-Parking: Spatio-Temporal Big Data Tells You Realtime Parking Availability (YR, ZX, RY, XM), pp. 646–654.
KDD-2018-Teh #learning #on the #problem: On Big Data Learning for Small Data Problems (YWT), p. 3.
ESEC-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.
ASPLOS-2018-NguyenFNXDL #distributed #named: Skyway: Connecting Managed Heaps in Distributed Big Data Systems (KN, LF, CN, G(X, BD, SL), pp. 56–69.
CASE-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.
CIG-2017-BertensGP #game studies #multi #predict #scalability: Games and big data: A scalable multi-dimensional churn prediction model (PB, AG, AP), pp. 33–36.
CIKM-2017-WangDCLG #named: CleanCloud: Cleaning Big Data on Cloud (HW, XD, XC, JL, HG), pp. 2543–2546.
KDD-2017-Berglund #mining: Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy (AB), p. 11.
KDD-2017-Cohen0Y #set: A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection (RC, LK0, AY), pp. 95–103.
KDD-2017-KarpatneK #challenge #machine learning: Big Data in Climate: Opportunities and Challenges for Machine Learning (AK, VK), pp. 21–22.
KDD-2017-Mazumdar #challenge #metric: Addressing Challenges with Big Data for Media Measurement (MM), p. 23.
KDD-2017-YanCKR #detection #distributed: Distributed Local Outlier Detection in Big Data (YY, LC, CK, EAR), pp. 1225–1234.
ESEC-FSE-2017-GarbervetskyP0M #optimisation #query #static analysis: Static analysis for optimizing big data queries (DG, ZP, MB0, MM, TM, EZ), pp. 932–937.
ASPLOS-2017-Zhou #data analysis: Big Data Analytics and Intelligence at Alibaba Cloud (JZ), p. 1.
CASE-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.
CASE-2017-ChenXZX #health #personalisation #quality: Big data analytic based personalized air quality health advisory model (LC, JX, LZ, YX), pp. 88–93.
CASE-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.
CASE-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.
CASE-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.
CASE-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.
WICSA-2016-KleinGAGGKNS #modelling: Model-Driven Observability for Big Data Storage (JK, IG, LA, JG, CG, RK, PN, VS), pp. 134–139.
CIKM-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.
CIKM-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.
CIKM-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.
CIKM-2016-ZhuLYZZGDRZ #locality: City-Scale Localization with Telco Big Data (FZ, CL, MY, YZ, ZZ, TG, KD, WR, JZ), pp. 439–448.
ICPR-2016-JoyR0V #optimisation #using: Hyperparameter tuning for big data using Bayesian optimisation (TTJ, SR, SG0, SV), pp. 2574–2579.
KDD-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.
KDD-2016-LiQJTYW #optimisation #parallel: Parallel Lasso Screening for Big Data Optimization (QL, SQ, SJ, PMT, JY, JW0), pp. 1705–1714.
KDD-2016-MoralesBKGF #data type #mining: IoT Big Data Stream Mining (GDFM, AB, LK, JG, WF0), pp. 2119–2120.
KDD-2016-SimoudisGGOS #lessons learnt: Big Data Needs Big Dreamers: Lessons from Successful Big Data Investors (ES, MG, TG, MO, GS), pp. 11–12.
KDD-2016-WangKGL: Crime Rate Inference with Big Data (HW0, DK, CG, ZL), pp. 635–644.
ASE-2016-LiLKLG #combinator #generative #testing: Applying combinatorial test data generation to big data applications (NL, YL, HRK, JL, YG), pp. 637–647.
FSE-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.
HT-2015-Smyth: From Small Sensors to Big Data (BS), p. 101.
JCDL-2015-KananZMF #learning #problem #summary: Big Data Text Summarization for Events: A Problem Based Learning Course (TK, XZ, MM, EAF), pp. 87–90.
JCDL-2015-XieCSWTK #data transformation #reuse #towards: Towards Use And Reuse Driven Big Data Management (ZX, YC, JS, TW, PAT, MK), pp. 65–74.
PODS-2015-FanGCDL #query: Querying Big Data by Accessing Small Data (WF, FG, YC, TD, PL), pp. 173–184.
PODS-2015-Jordan: Computational Thinking, Inferential Thinking and “Big Data” (MIJ), p. 1.
SIGMOD-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.
SIGMOD-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.
SIGMOD-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.
SIGMOD-2015-HuangZYDLND0Z #predict: Telco Churn Prediction with Big Data (YH, FZ, MY, KD, YL, BN, WD, QY, JZ), pp. 607–618.
SIGMOD-2015-KhayyatIJMOPQ0Y #named: BigDansing: A System for Big Data Cleansing (ZK, IFI, AJ, SM, MO, PP, JAQR, NT, SY), pp. 1215–1230.
SIGMOD-2015-RablDFSJ: Just can’t get enough: Synthesizing Big Data (TR, MD, MF, SS, HAJ), pp. 1457–1462.
SIGMOD-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.
SIGMOD-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.
VLDB-2015-Balazinska15a #data analysis #industrial #problem #question: Big Data Research: Will Industry Solve all the Problems? (MB), pp. 2053–2064.
VLDB-2015-HuYYDCYGZ #difference #framework #platform #privacy: Differential Privacy in Telco Big Data Platform (XH, MY, JY, YD, LC, QY, HG, JZ), pp. 1692–1703.
VLDB-2015-LoghinTZOT #performance: A Performance Study of Big Data on Small Nodes (DL, BMT, HZ, BCO, YMT), pp. 762–773.
VLDB-2015-SH #approach #named #testing: CODD: A Dataless Approach to Big Data Testing (AS, JRH), pp. 2008–2019.
VLDB-2015-Walter: Big Plateaus of Big Data on the Big Island (TW), pp. 2057–2068.
EDM-2015-MacHardyP #education #using: Evaluating Educational Videos using Bayesian Knowledge Tracing and Big Data (ZM, ZAP), pp. 424–427.
ICALP-v1-2015-Canonne #testing: Big Data on the Rise? — Testing Monotonicity of Distributions (CLC), pp. 294–305.
ICALP-v1-2015-Nikolov #database: An Improved Private Mechanism for Small Databases (AN), pp. 1010–1021.
DUXU-DD-2015-FanHV #risk management: Supply Chain Risk Management in the Era of Big Data (YF, LH, SV), pp. 283–294.
HIMI-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.
ICEIS-v1-2015-Aghbari #challenge #mining: Mining Big Data — Challenges and Opportunities (ZAA), pp. 379–384.
ICEIS-v1-2015-AnjosFBSGM #search-based: Genetic Mapping of Diseases through Big Data Techniques (JCSdA, BRF, JFB, RBS, CG, UM), pp. 279–286.
ICML-2015-EggelingKG: Dealing with small data: On the generalization of context trees (RE, MK, IG), pp. 1245–1253.
ICML-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.
KDD-2015-BifetMRHP #classification #data type #evaluation #online #performance: Efficient Online Evaluation of Big Data Stream Classifiers (AB, GDFM, JR, GH, BP), pp. 59–68.
KDD-2015-DhurandharGRME #risk management: Big Data System for Analyzing Risky Procurement Entities (AD, BG, RKR, GM, ME), pp. 1741–1750.
KDD-2015-FeldmanT #approximate #constraints #matrix: More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data (DF, TT), pp. 249–258.
KDD-2015-HsiehLZ #quality #recommendation: Inferring Air Quality for Station Location Recommendation Based on Urban Big Data (HPH, SDL, YZ), pp. 437–446.
KDD-2015-John #case study #how: How Artificial Intelligence and Big Data Created Rocket Fuel: A Case Study (GJ), p. 1629.
KDD-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.
KDD-2015-YangLJ #data analysis #optimisation: Big Data Analytics: Optimization and Randomization (TY, QL, RJ), p. 2327.
KDD-2015-ZhengYLLSCL #fine-grained #quality: Forecasting Fine-Grained Air Quality Based on Big Data (YZ, XY, ML, RL, ZS, EC, TL), pp. 2267–2276.
SKY-2015-Allalouf #library #mining #visualisation: Big Data in the Library: Extending Modern Library Catalogues with Data Visualization, Linking and Mining (MA), pp. 74–75.
ICSE-v2-2015-NagappanM #re-engineering: Big(ger) Data in Software Engineering (MN, MM), pp. 957–958.
ICSE-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.
SAC-2015-Rekha #detection #performance #using: A fast support vector data description system for anomaly detection using big data (AGR), pp. 931–932.
ASPLOS-2015-Gidra0SSN #garbage collection #named: NumaGiC: a Garbage Collector for Big Data on Big NUMA Machines (LG, GT, JS, MS, NN), pp. 661–673.
ASPLOS-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.
CASE-2015-LeeC: Aggregate production planning with small data in TFT-LCD manufacturing (CYL, MCC), pp. 647–648.
DATE-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.
DATE-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.
DATE-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.
DATE-2015-WangLZ #named: SODA: software defined FPGA based accelerators for big data (CW, XL, XZ), pp. 884–887.
ICST-2015-LiEGO #framework #scalability: A Scalable Big Data Test Framework (NL, AE, YG, JO), pp. 1–2.
DocEng-2014-SchmitzP #tool support: Humanist-centric tools for big data: berkeley prosopography services (PS, LP), pp. 179–188.
HT-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.
JCDL-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.
PODS-2014-FanGL #independence #on the #query: On scale independence for querying big data (WF, FG, LL), pp. 51–62.
SIGMOD-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.
SIGMOD-2014-IstvanWA #data flow: Histograms as a side effect of data movement for big data (ZI, LW, GA), pp. 1567–1578.
SIGMOD-2014-LeFevreSHTPC #data analysis #design #physics: Opportunistic physical design for big data analytics (JL, JS, HH, JT, NP, MJC), pp. 851–862.
SIGMOD-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.
SIGMOD-2014-OzcanTAKMRW #question: Are we experiencing a big data bubble? (FÖ, NT, DJA, MK, CM, KR, JLW), pp. 1407–1408.
SIGMOD-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.
SIGMOD-2014-ZoumpatianosIP #interactive: Indexing for interactive exploration of big data series (KZ, SI, TP), pp. 1555–1566.
VLDB-2014-CaoWR #data type #interactive: Interactive Outlier Exploration in Big Data Streams (LC, QW, EAR), pp. 1621–1624.
VLDB-2014-Jiang0OTW #named #scalability: epiC: an Extensible and Scalable System for Processing Big Data (DJ, GC, BCO, KLT, SW), pp. 541–552.
VLDB-2014-LeiZRE #framework #query: Redoop Infrastructure for Recurring Big Data Queries (CL, ZZ, EAR, MYE), pp. 1589–1592.
VLDB-2014-LiLZ #challenge #enterprise: Enterprise Search in the Big Data Era: Recent Developments and Open Challenges (YL, ZL, HZ), pp. 1717–1718.
VLDB-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.
VLDB-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.
VLDB-2014-SuchanekW #data analysis #knowledge base: Knowledge Bases in the Age of Big Data Analytics (FMS, GW), pp. 1713–1714.
VLDB-2014-SuSGOS #java: Changing Engines in Midstream: A Java Stream Computational Model for Big Data Processing (XS, GS, BG, BO, PS), pp. 1343–1354.
VLDB-2014-WuC0SCB #named: yzBigData: Provisioning Customizable Solution for Big Data (SW, GC, KC, LS, HC, HB), pp. 1778–1783.
VLDB-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.
VLDB-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.
VLDB-2015-GraefeVKKTLV14 #in memory #performance: In-Memory Performance for Big Data (GG, HV, HK, HAK, JT, ML, ACV), pp. 37–48.
EDM-2014-YudelsonFRBNJ: Better Data Beats Big Data (MY, SF, SR, SRB, TN, AJ), pp. 205–208.
ICSME-2014-SvajlenkoIKRM #benchmark #metric #towards: Towards a Big Data Curated Benchmark of Inter-project Code Clones (JS, JFI, IK, CKR, MMM), pp. 476–480.
DUXU-DI-2014-Bockermann #approach #data analysis #programming #visual notation: A Visual Programming Approach to Big Data Analytics (CB), pp. 393–404.
EDOC-2014-Ludwig #effectiveness #perspective: Managing Big Data Effectively — A Cloud Provider and a Cloud Consumer Perspective (HL), p. 91.
CIKM-2014-WangLBLGZ #named: Cleanix: A Big Data Cleaning Parfait (HW, ML, YB, JL, HG, JZ), pp. 2024–2026.
CIKM-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.
ECIR-2014-CarageaWCWRCWG #dataset: CiteSeer x : A Scholarly Big Dataset (CC, JW, AMC, KW, JPFR, HHC, ZW, CLG), pp. 311–322.
ICML-c2-2014-Chen0 #learning #modelling #topic #using: Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (ZC, BL), pp. 703–711.
ICML-c2-2014-DefazioDC #incremental #named #performance #problem: Finito: A faster, permutable incremental gradient method for big data problems (AD, JD, TSC), pp. 1125–1133.
KDD-2014-AnagnostopoulosT #scalability: Scaling out big data missing value imputations: pythia vs. godzilla (CA, PT), pp. 651–660.
KDD-2014-Chen0 #documentation #mining #topic: Mining topics in documents: standing on the shoulders of big data (ZC, BL), pp. 1116–1125.
KDD-2014-CormodeD #tutorial: Sampling for big data: a tutorial (GC, NGD), p. 1975.
KDD-2014-Eagle #social: Big data for social good (NE), p. 1522.
KDD-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.
KEOD-2014-Bergamaschi #challenge #integration #state of the art: Big Data Integration — State of the Art & Challenges (SB), pp. 1–7.
KEOD-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.
KEOD-2014-Talia #data mining #distributed #information management #mining: Big Data Mining Services and Distributed Knowledge Discovery Applications on Clouds (DT), pp. 1–5.
KMIS-2014-HeavinDA #information management: Small Data to Big Data — The Information Systems (IS) Continuum (CH, MD, FA), pp. 289–297.
SIGIR-2014-Williams #how: The data revolution: how companies are transforming with big data (HEW), pp. 525–526.
ASE-2014-StephenSSE #program analysis: Program analysis for secure big data processing (JJS, SS, RS, PTE), pp. 277–288.
SAC-2014-EvermannA #algorithm #implementation #mining #process: Big data meets process mining: implementing the alpha algorithm with map-reduce (JE, GA), pp. 1414–1416.
DATE-2014-0002LLCXY #data analysis #energy #network #performance: Energy efficient neural networks for big data analytics (YW, BL, RL, YC, NX, HY), pp. 1–2.
HPCA-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.
PDP-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.
ICLP-J-2014-TachmazidisAF #performance #semantics: Efficient Computation of the Well-Founded Semantics over Big Data (IT, GA, WF), pp. 445–459.
SIGMOD-2013-AboulnagaB #data analysis: Workload management for big data analytics (AA, SB), pp. 929–932.
SIGMOD-2013-BarnettCDDFGMP #exclamation #interactive: Stat!: an interactive analytics environment for big data (MB, BC, RD, SMD, DF, JG, PM, JCP), pp. 1013–1016.
SIGMOD-2013-CondieMPW #machine learning: Machine learning for big data (TC, PM, NP, MW), pp. 939–942.
SIGMOD-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.
SIGMOD-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.
SIGMOD-2013-NazarukR: Big data in capital markets (AN, MR), pp. 917–918.
SIGMOD-2013-SuchanekW: Knowledge harvesting in the big-data era (FMS, GW), pp. 933–938.
SIGMOD-2013-SumbalyKS #ecosystem: The big data ecosystem at LinkedIn (RS, JK, SS), pp. 1125–1134.
VLDB-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.
VLDB-2013-BellamkondaLJZLC #adaptation #execution #parallel: Adaptive and Big Data Scale Parallel Execution in Oracle (SB, HGL, UJ, YZ, VL, TC), pp. 1102–1113.
VLDB-2013-ChandramouliGQ #in the cloud #scalability: Scalable Progressive Analytics on Big Data in the Cloud (BC, JG, AQ), pp. 1726–1737.
VLDB-2013-DongS #integration: Big Data Integration (XLD, DS), pp. 1188–1189.
VLDB-2013-FanGN #preprocessor #query: Making Queries Tractable on Big Data with Preprocessing (WF, FG, FN), pp. 685–696.
VLDB-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.
VLDB-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.
VLDB-2013-SathiamoorthyAPDVCB #novel: XORing Elephants: Novel Erasure Codes for Big Data (MS, MA, DSP, AGD, RV, SC, DB), pp. 325–336.
VLDB-2013-TranBD #design #problem #query: Designing Query Optimizers for Big Data Problems of The Future (NT, SB, JD), pp. 1168–1169.
ICALP-v2-2013-BachrachP #performance #pseudo #recommendation #sketching #using: Sketching for Big Data Recommender Systems Using Fast Pseudo-random Fingerprints (YB, EP), pp. 459–471.
DUXU-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.
CIKM-2013-Giles #data mining #information management #mining: Scholarly big data: information extraction and data mining (CLG), pp. 1–2.
KDD-2013-CannyZ #data analysis: Big data analytics with small footprint: squaring the cloud (JC, HZ), pp. 95–103.
KDD-2013-GetoorM: Entity resolution for big data (LG, AM), p. 1527.
KDD-2013-Neumann #problem #using: Using “big data” to solve “small data” problems (CN), p. 1140.
KDD-2013-RamanSGJ #pipes and filters: Beyond myopic inference in big data pipelines (KR, AS, JG, TJ), pp. 86–94.
KDD-2013-SunR #data analysis: Big data analytics for healthcare (JS, CKR), p. 1525.
KDD-2013-ZhengLH #named #quality: U-Air: when urban air quality inference meets big data (YZ, FL, HPH), pp. 1436–1444.
MLDM-2013-Suthaharan #classification #network: A Single-Domain, Representation-Learning Model for Big Data Classification of Network Intrusion (SS), pp. 296–310.
SEKE-2013-Khoshgoftaar #challenge: Overcoming Big Data Challenges (TMK).
SIGIR-2013-Smith #multi: Riding the multimedia big data wave (JRS), pp. 1–2.
ICSE-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.
HPDC-2013-XuS0 #named: IBIS: interposed big-data I/O scheduler (YX, AS, MZ), pp. 109–110.
ISMM-2013-BuBXC #design: A bloat-aware design for big data applications (YB, VRB, G(X, MJC), pp. 119–130.
WICSA-ECSA-2012-BegoliH #design #effectiveness #information management: Design Principles for Effective Knowledge Discovery from Big Data (EB, JLH), pp. 215–218.
PODS-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.
SIGMOD-2012-ChengQR #data analysis #named: GLADE: big data analytics made easy (YC, CQ, FR), pp. 697–700.
VLDB-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.
VLDB-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.
VLDB-2012-DittrichQ #performance #pipes and filters: Efficient Big Data Processing in Hadoop MapReduce (JD, JAQR), pp. 2014–2015.
VLDB-2012-LabrinidisJ #challenge: Challenges and Opportunities with Big Data (AL, HVJ), pp. 2032–2033.
VLDB-2012-RablSJGMM #challenge #enterprise #performance: Solving Big Data Challenges for Enterprise Application Performance Management (TR, MS, HAJ, SGV, VMM, SM), pp. 1724–1735.
VLDB-2012-Shim #algorithm #data analysis #pipes and filters: MapReduce Algorithms for Big Data Analysis (KS), pp. 2016–2017.
VLDB-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.
ICML-2012-KleinerTSJ: The Big Data Bootstrap (AK, AT, PS, MIJ), p. 232.
KDD-2012-Jordan #divide and conquer #statistics: Divide-and-conquer and statistical inference for big data (MIJ), p. 4.
KDD-2012-Kitsuregawa: Building an engine for big data (MK), p. 223.
HPDC-2012-Budiu #artificial reality #framework #platform: Putting a “big-data” platform to good use: training kinect (MB), pp. 1–2.
VLDB-2011-Campbell #question: Is It Still “Big Data” If It Fits In My Pocket? (DC), p. 694.
SIGMOD-2010-Amer-YahiaDKKF #algorithm: Crowds, clouds, and algorithms: exploring the human side of “big data” applications (SAY, AD, JMK, NK, MJF), pp. 1259–1260.
VLDB-2010-AgrawalDA #in the cloud #question: Big Data and Cloud Computing: New Wine or just New Bottles? (DA, SD, AEA), pp. 1647–1648.
FSE-2010-Eagle #development #social: Big data, global development, and complex social systems (NE), pp. 3–4.
VLDB-2009-CohenDDHW #analysis: MAD Skills: New Analysis Practices for Big Data (JC, BD, MD, JMH, CW), pp. 1481–1492.
SAC-2000-AbiadHM #database #metric: Software Metrics for Small Database Applications (SA, RAH, NM), pp. 866–870.
CHI-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.
SOSP-1987-BirrellJW #database #implementation #performance: A Simple and Efficient Implementation for Small Databases (AB, MBJ, EW), pp. 149–154.