Stem top$ (all stems)

333 papers:

PODS-2015-RahulT #2d #on the

On Top-k Range Reporting in 2D Space (SR, YT), pp. 265–275.

SIGMOD-2015-ChenC

Diversity-Aware Top-k Publish/Subscribe for Text Stream (LC, GC), pp. 347–362.

SIGMOD-2015-JiangFW #keyword #network #scalability

Exact Top-k Nearest Keyword Search in Large Networks (MJ, AWCF, RCWW), pp. 393–404.

SIGMOD-2015-PengW #probability #query

k-Hit Query: Top-k Query with Probabilistic Utility Function (PP, RCWW), pp. 577–592.

SIGMOD-2015-PsallidasDCC #named #query

S4: Top-k Spreadsheet-Style Search for Query Discovery (FP, BD, KC, SC), pp. 2001–2016.

VLDB-2015-ChangLZYZQ #performance

Optimal Enumeration: Efficient Top-k Tree Matching (LC, XL, WZ, JXY, YZ, LQ), pp. 533–544.

VLDB-2015-DeutchGM #datalog #query #source code #using

Selective Provenance for Datalog Programs Using Top-K Queries (DD, AG, YM), pp. 1394–1405.

VLDB-2015-DingSMM #algorithm #framework #named #optimisation #problem

TOP: A Framework for Enabling Algorithmic Optimizations for Distance-Related Problems (YD, XS, MM, TM), pp. 1046–1057.

VLDB-2015-GaoL0ZZ #query

Answering Why-not Questions on Reverse Top-k Queries (YG, QL, GC, BZ, LZ), pp. 738–749.

VLDB-2015-SeahBS #concept #image #named #social #summary

PRISM: Concept-preserving Summarization of Top-K Social Image Search Results (BSS, SSB, AS), pp. 1868–1879.

SAS-2015-CastelnuovoNRSY #analysis #bottom-up #case study #composition #top-down

Modularity in Lattices: A Case Study on the Correspondence Between Top-Down and Bottom-Up Analysis (GC, MN, NR, MS, HY), pp. 252–274.

ICALP-v1-2015-GawrychowskiN #encoding

Optimal Encodings for Range Top- k k , Selection, and Min-Max (PG, PKN), pp. 593–604.

ICGT-2015-DrewesHM #parsing #predict #top-down

Predictive Top-Down Parsing for Hyperedge Replacement Grammars (FD, BH, MM), pp. 19–34.

CAiSE-2015-SunB #approach #clustering #novel #top-down

A Novel Top-Down Approach for Clustering Traces (YS, BB), pp. 331–345.

ICEIS-v1-2015-SarmentoCG #network #streaming #using

Streaming Networks Sampling using top-K Networks (RS, MC, JG), pp. 228–234.

ICEIS-v2-2015-TangL #framework #mining #product line #top-down

Top-down Feature Mining Framework for Software Product Line (YT, HL), pp. 71–81.

ECIR-2015-Dutta #approximate #mining #named #statistics #string #using

MIST: Top-k Approximate Sub-string Mining Using Triplet Statistical Significance (SD), pp. 284–290.

ICML-2015-ChenS #rank

Spectral MLE: Top-K Rank Aggregation from Pairwise Comparisons (YC, CS), pp. 371–380.

ICML-2015-KarN0 #precise

Surrogate Functions for Maximizing Precision at the Top (PK, HN, PJ), pp. 189–198.

ICML-2015-RajkumarGL0 #probability #ranking #set

Ranking from Stochastic Pairwise Preferences: Recovering Condorcet Winners and Tournament Solution Sets at the Top (AR, SG, LHL, SA), pp. 665–673.

KDD-2015-HayashiMTK #detection #realtime #topic #twitter

Real-Time Top-R Topic Detection on Twitter with Topic Hijack Filtering (KH, TM, MT, KiK), pp. 417–426.

KDD-2015-ZhangTMTJL #named #network #performance #scalability #similarity

Panther: Fast Top-k Similarity Search on Large Networks (JZ, JT, CM, HT, YJ, JL), pp. 1445–1454.

RecSys-2015-LimML #feedback #recommendation

Top-N Recommendation with Missing Implicit Feedback (DL, JM, GRGL), pp. 309–312.

RecSys-2015-VerstrepenG #recommendation

Top-N Recommendation for Shared Accounts (KV, BG), pp. 59–66.

ICSE-v2-2015-SchroederH0HLM #architecture #case study #design #evaluation #industrial #multi #product line #self

Design and Evaluation of a Customizable Multi-Domain Reference Architecture on Top of Product Lines of Self-Driving Heavy Vehicles — An Industrial Case Study (JS, DH, CB, CJH, LL, AM), pp. 189–198.

PODS-2014-Tao

A dynamic I/O-efficient structure for one-dimensional top-k range reporting (YT), pp. 256–265.

SIGMOD-2014-RanuHS #database #graph #query

Answering top-k representative queries on graph databases (SR, MXH, AKS), pp. 1163–1174.

SIGMOD-2014-TaoL #performance #similarity

Efficient top-K SimRank-based similarity join (WT, GL), pp. 1603–1604.

VLDB-2014-0002LW #elicitation #generative

Generating Top-k Packages via Preference Elicitation (MX, LVSL, PTW), pp. 1941–1952.

VLDB-2014-YuMS #random #using

Reverse Top-k Search using Random Walk with Restart (AWY, NM, HS), pp. 401–412.

VLDB-2015-DallachiesaPI14 #nearest neighbour #nondeterminism

Top-k Nearest Neighbor Search In Uncertain Data Series (MD, TP, IFI), pp. 13–24.

VLDB-2015-TaoYL14 #performance #similarity

Efficient Top-K SimRank-based Similarity Join (WT, MY, GL), pp. 317–328.

CSEET-2014-Koolmanojwong #re-engineering #risk management

Top-10 risks in real-client software engineering class projects (SK), pp. 201–202.

PLDI-2014-ZhangMNY #analysis #bottom-up #hybrid #interprocedural #top-down

Hybrid top-down and bottom-up interprocedural analysis (XZ, RM, MN, HY), p. 28.

STOC-2014-KumarS #all about #reduction

The limits of depth reduction for arithmetic formulas: it’s all about the top fan-in (MK, SS), pp. 136–145.

DLT-2014-EngelfrietMS #how #top-down #transducer

How to Remove the Look-Ahead of Top-Down Tree Transducers (JE, SM, HS), pp. 103–115.

LATA-2014-KoHS #top-down

Top-Down Tree Edit-Distance of Regular Tree Languages (SKK, YSH, KS), pp. 466–477.

ICEIS-v1-2014-Tribolet #adaptation #approach #bottom-up #enterprise #top-down

An Engineering Approach to Natural Enterprise Dynamics — From Top-down Purposeful Systemic Steering to Bottom-up Adaptive Guidance Control (JT), p. XIII.

CIKM-2014-LiZZW #keyword #named #performance

INK: A Cloud-Based System for Efficient Top-k Interval Keyword Search (RL, XZ, XZ, SW), pp. 2003–2005.

CIKM-2014-VouzoukidouAC #named #query #ranking #realtime

MeowsReader: Real-Time Ranking and Filtering of News with Generalized Continuous Top-k Queries (NV, BA, VC), pp. 2066–2068.

CIKM-2014-WuF14a #modelling #performance #query

Analytical Performance Modeling for Top-K Query Processing (HW, HF), pp. 1619–1628.

KDD-2014-LeeC

Top-k frequent itemsets via differentially private FP-trees (JL, CWC), pp. 931–940.

RecSys-2014-Aiolli #feedback #optimisation #recommendation

Convex AUC optimization for top-N recommendation with implicit feedback (FA), pp. 293–296.

RecSys-2014-Christakopoulou #independence #recommendation

Moving beyond linearity and independence in top-N recommender systems (EC), pp. 409–412.

RecSys-2014-LiuA #framework #recommendation #towards

Towards a dynamic top-N recommendation framework (XL, KA), pp. 217–224.

RecSys-2014-VanchinathanNBK #process #recommendation

Explore-exploit in top-N recommender systems via Gaussian processes (HPV, IN, FDB, AK), pp. 225–232.

SIGIR-2014-ZhangCT #keyword #query

Processing spatial keyword query as a top-k aggregation query (DZ, CYC, KLT), pp. 355–364.

HPDC-2014-ZhangJLGXI #in memory #memory management #named #programmable

TOP-PIM: throughput-oriented programmable processing in memory (DPZ, NJ, AL, JLG, LX, MI), pp. 85–98.

DATE-2013-JainTG #automation

Automated determination of top level control signals (RKJ, PT, SG), pp. 509–512.

SIGMOD-2013-KimS #algorithm #approximate #performance #string

Efficient top-k algorithms for approximate substring matching (YK, KS), pp. 385–396.

SIGMOD-2013-VlachouDNK #algorithm #bound #query

Branch-and-bound algorithm for reverse top-k queries (AV, CD, KN, YK), pp. 481–492.

VLDB-2013-BoghSJ #approach #named #retrieval

GroupFinder: A New Approach to Top-K Point-of-Interest Group Retrieval (KSB, AS, CSJ), pp. 1226–1229.

VLDB-2013-FanWW #graph #pattern matching

Diversified Top-k Graph Pattern Matching (WF, XW, YW), pp. 1510–1521.

VLDB-2013-FenderM #top-down

Counter Strike: Generic Top-Down Join Enumeration for Hypergraphs (PF, GM), pp. 1822–1833.

VLDB-2013-HuangCLQY #network #scalability

Top-K Structural Diversity Search in Large Networks (XH, HC, RHL, LQ, JXY), pp. 1618–1629.

VLDB-2013-QiaoQCYT #graph #keyword #scalability

Top-K Nearest Keyword Search on Large Graphs (MQ, LQ, HC, JXY, WT), pp. 901–912.

VLDB-2013-ShraerGFJ #social

Top-k Publish-Subscribe for Social Annotation of News (AS, MG, MF, VJ), pp. 385–396.

VLDB-2014-ChenHX13 #authentication #query

Authenticating Top-k Queries in Location-based Services with Confidentiality (QC, HH, JX), pp. 49–60.

DLT-2013-FulopM #composition #linear #top-down #transducer

Composition Closure of ε-Free Linear Extended Top-Down Tree Transducers (ZF, AM), pp. 239–251.

ICALP-v1-2013-BilleGLW

Tree Compression with Top Trees (PB, ILG, GML, OW), pp. 160–171.

CHI-2013-LeeOIB #2d #3d #interactive #named

SpaceTop: integrating 2D and spatial 3D interactions in a see-through desktop environment (JL, AO, HI, CNB), pp. 189–192.

HCI-IMT-2013-KurosawaST

Keyboard Clawing: Input Method by Clawing Key Tops (TK, BS, JT), pp. 272–280.

CIKM-2013-LanNGC #question #ranking

Is top-k sufficient for ranking? (YL, SN, JG, XC), pp. 1261–1270.

ICML-c3-2013-Busa-FeketeSCWH #adaptation

Top-k Selection based on Adaptive Sampling of Noisy Preferences (RBF, BS, WC, PW, EH), pp. 1094–1102.

ICML-c3-2013-LakshminarayananRT #top-down

Top-down particle filtering for Bayesian decision trees (BL, DMR, YWT), pp. 280–288.

KDD-2013-KabburNK #modelling #named #recommendation #similarity

FISM: factored item similarity models for top-N recommender systems (SK, XN, GK), pp. 659–667.

RecSys-2013-Aiolli #dataset #performance #recommendation #scalability

Efficient top-n recommendation for very large scale binary rated datasets (FA), pp. 273–280.

RecSys-2013-Ben-Shimon #algorithm #recommendation

Anytime algorithms for top-N recommenders (DBS), pp. 463–466.

RecSys-2013-CremonesiGQ #recommendation

Evaluating top-n recommendations “when the best are gone” (PC, FG, MQ), pp. 339–342.

RecSys-2013-HuY #learning #process #recommendation

Interview process learning for top-n recommendation (FH, YY), pp. 331–334.

RecSys-2013-OstuniNSM #feedback #linked data #open data #recommendation

Top-N recommendations from implicit feedback leveraging linked open data (VCO, TDN, EDS, RM), pp. 85–92.

RecSys-2013-SuYCY #personalisation #ranking #recommendation

Set-oriented personalized ranking for diversified top-n recommendation (RS, LY, KC, YY), pp. 415–418.

SIGIR-2013-DimopoulosNS #performance #query

A candidate filtering mechanism for fast top-k query processing on modern cpus (CD, SN, TS), pp. 723–732.

SIGIR-2013-ZhangCWY #collaboration #optimisation

Optimizing top-n collaborative filtering via dynamic negative item sampling (WZ, TC, JW, YY), pp. 785–788.

RE-2013-NcubeLD #challenge #identification #requirements #research

Identifying top challenges for international research on requirements engineering for systems of systems engineering (CN, SLL, HD), pp. 342–344.

SAC-2013-Fournier-VigerT #mining #named

TNS: mining top-k non-redundant sequential rules (PFV, VST), pp. 164–166.

WICSA-ECSA-2012-ElorantaHVK #architecture #documentation #generative #knowledge base #named #topic #using

TopDocs: Using Software Architecture Knowledge Base for Generating Topical Documents (VPE, OH, TV, KK), pp. 191–195.

PODS-2012-ShengT #memory management

Dynamic top-k range reporting in external memory (CS, YT), pp. 121–130.

SIGMOD-2012-FraternaliMT #bound

Top-k bounded diversification (PF, DM, MT), pp. 421–432.

SIGMOD-2012-LuSLDWC #generative

Optimal top-k generation of attribute combinations based on ranked lists (JL, PS, CL, XD, SW, XC), pp. 409–420.

SIGMOD-2012-YuAY #query #scalability

Processing a large number of continuous preference top-k queries (AY, PKA, JY), pp. 397–408.

VLDB-2012-FujiwaraNOK #performance #random

Fast and Exact Top-k Search for Random Walk with Restart (YF, MN, MO, MK), pp. 442–453.

VLDB-2012-QinYC

Diversifying Top-K Results (LQ, JXY, LC), pp. 1124–1135.

VLDB-2013-MouratidisP12 #query

Computing Immutable Regions for Subspace Top-k Queries (KM, HP), pp. 73–84.

PLDI-2012-AlbarghouthiKNR #analysis #interprocedural #top-down

Parallelizing top-down interprocedural analyses (AA, RK, AVN, SKR), pp. 217–228.

STOC-2012-GuptaKL #multi #re-engineering

Reconstruction of depth-4 multilinear circuits with top fan-in 2 (AG, NK, SVL), pp. 625–642.

CIKM-2012-CamposDJN #approach #higher-order #identification #named #web

GTE: a distributional second-order co-occurrence approach to improve the identification of top relevant dates in web snippets (RC, GD, AJ, CN), pp. 2035–2039.

CIKM-2012-HaKKFP #recommendation

Top-N recommendation through belief propagation (JH, SHK, SWK, CF, SP), pp. 2343–2346.

CIKM-2012-HuangLTF #keyword #performance #query

Efficient safe-region construction for moving top-K spatial keyword queries (WH, GL, KLT, JF), pp. 932–941.

CIKM-2012-LowZ #matrix #performance #query #similarity

Fast top-k similarity queries via matrix compression (YL, AXZ), pp. 2070–2074.

CIKM-2012-NiuLGC #probability #problem #ranking

A new probabilistic model for top-k ranking problem (SN, YL, JG, XC), pp. 2519–2522.

CIKM-2012-PanZ12a #graph #query

Continuous top-k query for graph streams (SP, XZ), pp. 2659–2662.

CIKM-2012-StuparM #query

Being picky: processing top-k queries with set-defined selections (AS, SM), pp. 912–921.

CIKM-2012-WangZCM #network #retrieval #using

Top-k retrieval using conditional preference networks (HW, XZ, WC, PM), pp. 2075–2079.

CIKM-2012-ZhanZZL #nondeterminism

Finding top k most influential spatial facilities over uncertain objects (LZ, YZ, WZ, XL), pp. 922–931.

ECIR-2012-ZucconAZW #analysis #retrieval #using

Top-k Retrieval Using Facility Location Analysis (GZ, LA, DZ, JW), pp. 305–316.

ICPR-2012-BaiZX #detection #linear #multi

Multi scale multi structuring element top-hat transform for linear feature detection (XB, FZ, BX), pp. 1920–1923.

ICPR-2012-LinL #bottom-up #process #top-down

Integrating bottom-up and top-down processes for accurate pedestrian counting (YL, NL), pp. 2508–2511.

ICPR-2012-Meyer

Made to measure top hats (FM), pp. 3164–3167.

ICPR-2012-PanZF #correlation #data type #query

Top-k correlated subgraph query for data streams (SP, XZ, MF), pp. 2906–2909.

ICPR-2012-ShaukatGWB #approach #bottom-up #detection #top-down

Meeting in the Middle: A top-down and bottom-up approach to detect pedestrians (AS, AG, DW, RB), pp. 874–877.

ICPR-2012-YamasakiC #classification #recognition #refinement

Confidence-assisted classification result refinement for object recognition featuring TopN-Exemplar-SVM (TY, TC), pp. 1783–1786.

KDD-2012-WuSTY #mining

Mining top-K high utility itemsets (CWW, BES, VST, PSY), pp. 78–86.

KDD-2012-ZhangZW #web

A system for extracting top-K lists from the web (ZZ, KQZ, HW), pp. 1560–1563.

KDIR-2012-SorkhiAHH #framework #game studies #identification #network #social

A Game-Theoretic Framework to Identify Top-K Teams in Social Networks (MS, HA, SH, AH), pp. 252–257.

MLDM-2012-LiHO #approach #correlation #mining

Top-N Minimization Approach for Indicative Correlation Change Mining (AL, MH, YO), pp. 102–116.

RecSys-2012-Diaz-AvilesDSN #realtime #recommendation #social

Real-time top-n recommendation in social streams (EDA, LD, LST, WN), pp. 59–66.

RecSys-2012-NingK #linear #recommendation

Sparse linear methods with side information for top-n recommendations (XN, GK), pp. 155–162.

RecSys-2012-YangSGL #network #on the #recommendation #social #using

On top-k recommendation using social networks (XY, HS, YG, YL), pp. 67–74.

SIGIR-2012-CulpepperPS #documentation #in memory #performance #retrieval

Efficient in-memory top-k document retrieval (JSC, MP, FS), pp. 225–234.

SIGIR-2012-LiWLF #performance #query

Supporting efficient top-k queries in type-ahead search (GL, JW, CL, JF), pp. 355–364.

SIGIR-2012-NiuGLC #evaluation #learning #rank #ranking

Top-k learning to rank: labeling, ranking and evaluation (SN, JG, YL, XC), pp. 751–760.

SIGIR-2012-ShiKBLHO #named #optimisation #recommendation

TFMAP: optimizing MAP for top-n context-aware recommendation (YS, AK, LB, ML, AH, NO), pp. 155–164.

SAC-2012-PedreschiRT #case study #metric

A study of top-k measures for discrimination discovery (DP, SR, FT), pp. 126–131.

VLDB-2011-FontouraJLVZZ #evaluation #query

Evaluation Strategies for Top-k Queries over Memory-Resident Inverted Indexes (MF, VJ, JL, SV, XZ, JYZ), pp. 1213–1224.

VLDB-2011-SunHYYW #named #network #similarity

PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks (YS, JH, XY, PSY, TW), pp. 992–1003.

VLDB-2011-WangYYZP #database #nondeterminism #on the #ranking

On Pruning for Top-K Ranking in Uncertain Databases (CW, LYY, JHY, ORZ, JP), pp. 598–609.

VLDB-2011-ZhuQLYHY #mining #network #scalability

Mining Top-K Large Structural Patterns in a Massive Network (FZ, QQ, DL, XY, JH, PSY), pp. 807–818.

VLDB-2012-RanuS11 #query

Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions (SR, AKS), pp. 169–180.

STOC-2011-SaxenaS #bound #matter #testing

Blackbox identity testing for bounded top fanin depth-3 circuits: the field doesn’t matter (NS, CS), pp. 431–440.

LATA-2011-LaurenceLNST #normalisation #top-down #transducer

Normalization of Sequential Top-Down Tree-to-Word Transducers (GL, AL, JN, SS, MT), pp. 354–365.

CSCW-2011-ConversyGCVDO #collaboration

Supporting air traffic control collaboration with a TableTop system (SC, HGB, SC, SV, CD, CO), pp. 425–434.

ICEIS-v1-2011-LuoYHJCH #algorithm #named #query

IRTA: An Improved Threshold Algorithm for Reverse Top-k Queries (CL, FY, WCH, ZJ, DC, SH), pp. 135–140.

CIKM-2011-HuangWQZCH

Top-k most influential locations selection (JH, ZW, JQ, RZ, JC, ZH), pp. 2377–2380.

CIKM-2011-KargarA #network #social

Discovering top-k teams of experts with/without a leader in social networks (MK, AA), pp. 985–994.

CIKM-2011-LadwigT #algorithm #database #keyword

Index structures and top-k join algorithms for native keyword search databases (GL, TT), pp. 1505–1514.

CIKM-2011-LiangXL

Adding structure to top-k: from items to expansions (XL, MX, LVSL), pp. 1699–1708.

CIKM-2011-LiuYS #dataset #query

Subject-oriented top-k hot region queries in spatial dataset (JL, GY, HS), pp. 2409–2412.

CIKM-2011-MetzgerEHS #named

S3K: seeking statement-supporting top-K witnesses (SM, SE, KH, RS), pp. 37–46.

CIKM-2011-ShastriDRW #multi #named #scalability

MTopS: scalable processing of continuous top-k multi-query workloads (AS, DY, EAR, MOW), pp. 1107–1116.

CIKM-2011-YuKL #approach #bidirectional #bottom-up #information management #top-down #towards

Towards a top-down and bottom-up bidirectional approach to joint information extraction (XY, IK, MRL), pp. 847–856.

RecSys-2011-PraweshP #recommendation

The “top N” news recommender: count distortion and manipulation resistance (SP, BP), pp. 237–244.

RecSys-2011-YuanCZ #recommendation #social

Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation (QY, LC, SZ), pp. 245–252.

SIGIR-2011-Campos #analysis #query #using #web

Using k-Top retrieved web snippets to date temporalimplicit queries based on web content analysis (RNTC), pp. 1325–1326.

SIGIR-2011-DingS #documentation #performance #retrieval #using

Faster top-k document retrieval using block-max indexes (SD, TS), pp. 993–1002.

SAC-2011-BenouaretBH #approach #composition #fuzzy

Top-k service compositions: a fuzzy set-based approach (KB, DB, AH), pp. 1033–1038.

SAC-2011-DabringerE #performance #query #retrieval

Efficient top-k retrieval for user preference queries (CD, JE), pp. 1045–1052.

SAC-2011-WangJZO #database #graph #keyword

Exact top-k keyword search on graph databases (MW, LJ, LZ, TO), pp. 985–986.

ASPLOS-2011-PorterBHOH #library #top-down

Rethinking the library OS from the top down (DEP, SBW, JH, RO, GCH), pp. 291–304.

CADE-2011-BaaderBBM #concept #logic #unification

Unification in the Description Logic EL without the Top Concept (FB, TBN, SB, BM), pp. 70–84.

PODS-2010-LemayMN #algorithm #learning #top-down #xml

A learning algorithm for top-down XML transformations (AL, SM, JN), pp. 285–296.

SIGMOD-2010-UMBB #documentation

Durable top-k search in document archives (LHU, NM, KB, SJB), pp. 555–566.

VLDB-2010-CaoCJ #web

Retrieving Top-k Prestige-Based Relevant Spatial Web Objects (XC, GC, CSJ), pp. 373–384.

VLDB-2010-Chen #named #performance #pipes and filters

Cheetah: A High Performance, Custom Data Warehouse on Top of MapReduce (SC), pp. 1459–1468.

VLDB-2010-DeutchMPY #evaluation #process #query

Optimal Top-K Query Evaluation for Weighted Business Processes (DD, TM, NP, TY), pp. 940–951.

VLDB-2010-VlachouDNK #identification #query

Identifying the Most Influential Data Objects with Reverse Top-k Queries (AV, CD, KN, YK), pp. 364–372.

VLDB-2010-WuBMPS #query

Processing Top-k Join Queries (MW, LBE, AM, CMP, DS), pp. 860–870.

VLDB-2011-Rocha-JuniorVDN10 #performance #query

Efficient Processing of Top-k Spatial Preference Queries (JBRJ, AV, CD, KN), pp. 93–104.

STOC-2010-KarninMSV #bound #multi #testing

Deterministic identity testing of depth-4 multilinear circuits with bounded top fan-in (ZSK, PM, AS, IV), pp. 649–658.

DLT-2010-Maletti #top-down #transducer

Input Products for Weighted Extended Top-Down Tree Transducers (AM), pp. 316–327.

CIKM-2010-ChenLZY #energy #network #query

Energy-efficient top-k query processing in wireless sensor networks (BC, WL, RZ, JXY), pp. 329–338.

CIKM-2010-ChrysakisCP #algorithm #evaluation #network #peer-to-peer #query #using

Evaluation of top-k queries in peer-to-peer networks using threshold algorithms (IC, CC, DP), pp. 1305–1308.

CIKM-2010-GaoQJWY #graph #performance

Fast top-k simple shortest paths discovery in graphs (JG, HQ, XJ, TW, DY), pp. 509–518.

CIKM-2010-GeXZOYL #detection #evolution #named

Top-Eye: top-k evolving trajectory outlier detection (YG, HX, ZHZ, HTO, JY, KCL), pp. 1733–1736.

CIKM-2010-HaghaniMA #personalisation #web

The gist of everything new: personalized top-k processing over web 2.0 streams (PH, SM, KA), pp. 489–498.

CIKM-2010-KimKLB #interpreter #performance #semantics

Efficient wikipedia-based semantic interpreter by exploiting top-k processing (JWK, AK, DL, SB), pp. 1813–1816.

CIKM-2010-ShalemK

Computing the top-k maximal answers in a join of ranked lists (MS, YK), pp. 1381–1384.

CIKM-2010-YinLL #on the #social #web

On top-k social web search (PY, WCL, KCKL), pp. 1313–1316.

ICPR-2010-KasiviswanathanBS #analysis #documentation #top-down

Top Down Analysis of Line Structure in Handwritten Documents (HK, GRB, SNS), pp. 2025–2028.

ICPR-2010-LiHLYZL #recognition #top-down

Event Recognition Based on Top-Down Motion Attention (LL, WH, BL, CY, PZ, WL), pp. 3561–3564.

ICPR-2010-SangWW #learning #modelling #top-down #visual notation

A Biologically-Inspired Top-Down Learning Model Based on Visual Attention (NS, LW, YW), pp. 3736–3739.

ICPR-2010-WangAYL #bottom-up #estimation #learning #top-down #using

Combined Top-Down/Bottom-Up Human Articulated Pose Estimation Using AdaBoost Learning (SW, HA, TY, SL), pp. 3670–3673.

KDD-2010-LamC #data type #flexibility #mining

Mining top-k frequent items in a data stream with flexible sliding windows (HTL, TC), pp. 283–292.

KDD-2010-WangCSX #algorithm #mining #mobile #network #social

Community-based greedy algorithm for mining top-K influential nodes in mobile social networks (YW, GC, GS, KX), pp. 1039–1048.

RecSys-2010-CremonesiKT #algorithm #performance #recommendation

Performance of recommender algorithms on top-n recommendation tasks (PC, YK, RT), pp. 39–46.

SEKE-2010-MadieshW #process #top-down

A Top-Down Method for Secure SOA-based B2B Processes (MM, GW), pp. 698–703.

SIGIR-2010-LeeJSL #identification #mining

Mining the blogosphere for top news stories identification (YL, HYJ, WS, JHL), pp. 395–402.

ECMFA-2010-CregutCPFP #animation #framework #generative

Generative Technologies for Model Animation in the TopCased Platform (XC, BC, MP, RF, JP), pp. 90–103.

SAC-2010-JungCCL #on the #query

On processing location based top-k queries in the wireless broadcasting system (HJ, BKC, YDC, LL), pp. 585–591.

LICS-2010-PopescuGO #normalisation #system f

Strong Normalization for System F by HOAS on Top of FOAS (AP, ELG, CJO), pp. 31–40.

ICDAR-2009-SaldarriagaMV #documentation #online #recognition #using

Using top n Recognition Candidates to Categorize On-line Handwritten Documents (SPS, EM, CVG), pp. 881–885.

SIGMOD-2009-GeZM #nondeterminism #query

Top-k queries on uncertain data: on score distribution and typical answers (TG, SBZ, SM), pp. 375–388.

SIGMOD-2009-KimC #documentation #named #performance #query #retrieval

Skip-and-prune: cosine-based top-k query processing for efficient context-sensitive document retrieval (JWK, KSC), pp. 115–126.

SIGMOD-2009-RadwanPSY #generative

Top-k generation of integrated schemas based on directed and weighted correspondences (AR, LP, IRS, AAY), pp. 641–654.

VLDB-2009-CongJW #performance #retrieval #web

Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects (GC, CSJ, DW), pp. 337–348.

VLDB-2009-GatesNCKNORSS #data flow #experience #pipes and filters

Building a HighLevel Dataflow System on top of MapReduce: The Pig Experience (AG, ON, SC, PK, SN, CO, BR, SS, US), pp. 1414–1425.

VLDB-2009-HeN #top-down

Anonymization of Set-Valued Data via Top-Down, Local Generalization (YH, JFN), pp. 934–945.

VLDB-2009-LeglerLSK #distributed #mining #robust #using

Robust Distributed Top-N Frequent Pattern Mining Using the SAP BW Accelerator (TL, WL, JS, JK), pp. 1438–1449.

ICEIS-DISI-2009-GorawskiD #algorithm #distributed #execution #performance #query

Algorithms for Efficient Top-K Spatial Preference Query Execution in a Heterogeneous Distributed Environment (MG, KD), pp. 43–48.

CIKM-2009-HaghaniMA #data type #query #semistructured data

Evaluating top-k queries over incomplete data streams (PH, SM, KA), pp. 877–886.

CIKM-2009-LeeLKL #mobile #named #web

AnchorWoman: top-k structured mobile web search engine (WL, JJHL, YKK, CKSL), pp. 2089–2090.

MLDM-2009-OkuboH #concept #pseudo

Finding Top-N Pseudo Formal Concepts with Core Intents (YO, MH), pp. 479–493.

RecSys-2009-JamaliE #network #recommendation #trust #using

Using a trust network to improve top-N recommendation (MJ, ME), pp. 181–188.

RecSys-2009-SeyerlehnerFW #on the #recommendation

On the limitations of browsing top-N recommender systems (KS, AF, GW), pp. 321–324.

RecSys-2009-Zhang #recommendation

Enhancing diversity in Top-N recommendation (MZ), pp. 397–400.

PADL-2009-PereiraP #logic programming #modelling #query #source code #top-down

Layered Models Top-Down Querying of Normal Logic Programs (LMP, AMP), pp. 254–268.

SIGMOD-2008-VlachouDNV #distributed #on the #performance #query

On efficient top-k query processing in highly distributed environments (AV, CD, KN, MV), pp. 753–764.

SIGMOD-2008-VuOPT #database #graph #keyword

A graph method for keyword-based selection of the top-K databases (QHV, BCO, DP, AKHT), pp. 915–926.

VLDB-2008-BeskalesSI #database #nearest neighbour #nondeterminism #performance

Efficient search for the top-k probable nearest neighbors in uncertain databases (GB, MAS, IFI), pp. 326–339.

VLDB-2008-GuoARSSV #performance

Efficient top-k processing over query-dependent functions (LG, SAY, RR, JS, US, EV), pp. 1044–1055.

VLDB-2008-JinYCYL #nondeterminism #query

Sliding-window top-k queries on uncertain streams (CJ, KY, LC, JXY, XL), pp. 301–312.

ICPR-2008-MooreSLD #image #segmentation #top-down #using

Top down image segmentation using congealing and graph-cut (DM, JS, SL, BAD), pp. 1–4.

RecSys-2008-Kwon #rating #recommendation #using

Improving top-n recommendation techniques using rating variance (YK), pp. 307–310.

SIGIR-2008-SchenkelCKMNPW #network #performance #query

Efficient top-k querying over social-tagging networks (RS, TC, MK, SM, TN, JXP, GW), pp. 523–530.

ICLP-2008-MuggletonST #bias #declarative #logic programming #named #using

TopLog: ILP Using a Logic Program Declarative Bias (SM, JCAS, ATN), pp. 687–692.

ICLP-2008-Santos #bias #declarative #logic programming #named #using

TopLog: ILP Using a Logic Program Declarative Bias (JCAS), pp. 818–819.

DAC-2007-GandikotaCBSB #analysis #set

Top-k Aggressors Sets in Delay Noise Analysis (RG, KC, DB, DS, MRB), pp. 174–179.

DATE-2007-CrepaldiCGZ #design #effectiveness #top-down

An effective AMS top-down methodology applied to the design of a mixed-signal UWB system-on-chip (MC, MRC, MG, MZ), pp. 1424–1429.

ICDAR-2007-CaoPNM #bottom-up #fault #robust #segmentation #top-down

Robust Page Segmentation Based on Smearing and Error Correction Unifying Top-down and Bottom-up Approaches (HC, RP, PN, EM), pp. 392–396.

SIGMOD-2007-DeHaanT #top-down

Optimal top-down join enumeration (DD, FWT), pp. 785–796.

SIGMOD-2007-LuoLWZ #database #keyword #named #query #relational

Spark: top-k keyword query in relational databases (YL, XL, WW, XZ), pp. 115–126.

SIGMOD-2007-SolimanIC #database #evaluation #named #nondeterminism #performance #query

URank: formulation and efficient evaluation of top-k queries in uncertain databases (MAS, IFI, KCCC), pp. 1082–1084.

SIGMOD-2007-TheobaldSW #information retrieval

The TopX DB&IR engine (MT, RS, GW), pp. 1141–1143.

SIGMOD-2007-XinHC #ad hoc #ranking

Progressive and selective merge: computing top-k with ad-hoc ranking functions (DX, JH, KCCC), pp. 103–114.

VLDB-2007-AkbariniaPV #algorithm #query

Best Position Algorithms for Top-k Queries (RA, EP, PV), pp. 495–506.

VLDB-2007-AraiDGK #algorithm #metric

Anytime Measures for Top-k Algorithms (BA, GD, DG, NK), pp. 914–925.

VLDB-2007-DasGKS #ad hoc #data type #query

Ad-hoc Top-k Query Answering for Data Streams (GD, DG, NK, NS), pp. 183–194.

VLDB-2007-DeRoseSCDR #approach #community #composition #incremental #top-down #web

Building Structured Web Community Portals: A Top-Down, Compositional, and Incremental Approach (PD, WS, FC, AD, RR), pp. 399–410.

VLDB-2007-HuaPFLL #database #query #scalability

Efficiently Answering Top-k Typicality Queries on Large Databases (MH, JP, AWCF, XL, HfL), pp. 890–901.

VLDB-2007-LiH #approximate #mining #multi

Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data (XL, JH), pp. 447–458.

VLDB-2007-QiCS #graph #query

Sum-Max Monotonic Ranked Joins for Evaluating Top-K Twig Queries on Weighted Data Graphs (YQ, KSC, MLS), pp. 507–518.

VLDB-2007-YiuM #multi #performance #query

Efficient Processing of Top-k Dominating Queries on Multi-Dimensional Data (MLY, NM), pp. 483–494.

WCRE-2007-SchaferAMMO #clustering #framework #generative

Clustering for Generating Framework Top-Level Views (TS, IA, MM, MM, KO), pp. 239–248.

LATA-2007-Maletti #composition #top-down #transducer

Compositions of Extended Top-down Tree Transducers (AM), pp. 379–390.

OCSC-2007-CarcilloR #bottom-up #classification #top-down

Tags for Citizens: Integrating Top-Down and Bottom-Up Classification in the Turin Municipality Website (FC, LR), pp. 256–264.

EDOC-2007-RosenbergEMPD #aspect-oriented #development #process #quality #top-down #using

Integrating Quality of Service Aspects in Top-Down Business Process Development Using WS-CDL and WS-BPEL (FR, CE, AM, CP, SD), pp. 15–26.

ICEIS-EIS-2007-MurzekK #model transformation #process

Business Process Model Transformation Issues — The Top 7 Adversaries Encountered at Defining Model Transformations (MM, GK), pp. 144–151.

CIKM-2007-ZhuSLW #documentation #effectiveness

Effective top-k computation in retrieving structured documents with term-proximity support (MZ, SS, ML, JRW), pp. 771–780.

ICSE-2007-HonidenTYTW #architecture #development #re-engineering #tool support

Top SE: Educating Superarchitects Who Can Apply Software Engineering Tools to Practical Development in Japan (SH, YT, NY, KT, HW), pp. 708–718.

DATE-2006-MartensE #synthesis #top-down

Top-down heterogeneous synthesis of analog and mixed-signal systems (EM, GGEG), pp. 275–280.

PODS-2006-KimelfeldS #approximate #keyword #proximity

Finding and approximating top-k answers in keyword proximity search (BK, YS), pp. 173–182.

SIGMOD-2006-MouratidisBP #monitoring #query

Continuous monitoring of top-k queries over sliding windows (KM, SB, DP), pp. 635–646.

VLDB-2006-BastMSTW #named #query

IO-Top-k: Index-access Optimized Top-k Query Processing (HB, DM, RS, MT, GW), pp. 475–486.

VLDB-2006-DasGKT #query #using

Answering Top-k Queries Using Views (GD, DG, NK, DT), pp. 451–462.

VLDB-2006-XinHCL #approach #multi #query #ranking

Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach (DX, JH, HC, XL), pp. 463–475.

SFM-2006-CimattiS #performance #satisfiability

Building Efficient Decision Procedures on Top of SAT Solvers (AC, RS), pp. 144–175.

ICPR-v4-2006-TeshimaSOYI #estimation #image

Vehicle Lateral Position Estimation Method Based on Matching of Top-View Images (TT, HS, SO, KY, TI), pp. 626–629.

KDD-2006-ArunasalamC #classification #named #top-down

CCCS: a top-down associative classifier for imbalanced class distribution (BA, SC), pp. 517–522.

KDD-2006-Kahn #problem #statistics

Capital One’s statistical problems: our top ten list (WK), p. 834.

KDD-2006-XinCYH

Extracting redundancy-aware top-k patterns (DX, HC, XY, JH), pp. 444–453.

PADL-2006-NavasBH #analysis #clique #performance #top-down #using

Efficient Top-Down Set-Sharing Analysis Using Cliques (JAN, FB, MVH), pp. 183–198.

ICSE-2006-KojarskiL #approach #modelling #top-down

Modeling aspect mechanisms: a top-down approach (SK, DHL), pp. 212–221.

PPoPP-2006-SharapovKDCR #case study #estimation #parallel #performance #scalability #top-down

A case study in top-down performance estimation for a large-scale parallel application (IS, RK, GD, RC, MR), pp. 81–89.

DATE-2005-MullerTAL #design #multi #power management #top-down

Top-Down Design of a Low-Power Multi-Channel 2.5-Gbit/s/Channel Gated Oscillator Clock-Recovery Circuit (PM, AT, SMA, YL), pp. 258–263.

SIGMOD-2005-CongTTX #mining

Mining Top-k Covering Rule Groups for Gene Expression Data (GC, KLT, AKHT, XX), pp. 670–681.

SIGMOD-2005-LiCIS #algebra #named #optimisation #query #relational

RankSQL: Query Algebra and Optimization for Relational Top-k Queries (CL, KCCC, IFI, SS), pp. 131–142.

VLDB-2005-MichelTW #algorithm #distributed #framework #named #query

KLEE: A Framework for Distributed Top-k Query Algorithms (SM, PT, GW), pp. 637–648.

VLDB-2005-TheobaldSW #performance #query

An Efficient and Versatile Query Engine for TopX Search (MT, RS, GW), pp. 625–636.

VLDB-2005-XiaZKD #on the

On Computing Top-t Most Influential Spatial Sites (TX, DZ, EK, YD), pp. 946–957.

ICSM-2005-HassanH #fault #predict

The Top Ten List: Dynamic Fault Prediction (AEH, RCH), pp. 263–272.

STOC-2005-Basu #algebra #algorithm #polynomial #set

Polynomial time algorithm for computing the top Betti numbers of semi-algebraic sets defined by quadratic inequalities (SB), pp. 313–322.

CIAA-2005-SudaH #algorithm #automaton #backtracking #top-down

Non-backtracking Top-Down Algorithm for Checking Tree Automata Containment (TS, HH), pp. 294–306.

ICEIS-v1-2005-SimonssonLJNGW #approach #enterprise #evaluation #top-down

Scenario-based Evaluation of Enterprise — a Top-Down Approach for Chief Information Officer Decision Making (MS, ÅL, PJ, LN, JG, OW), pp. 130–137.

ECIR-2005-YangJZY #documentation #effectiveness #retrieval #using

Improving Retrieval Effectiveness by Using Key Terms in Top Retrieved Documents (LY, DHJ, GZ, NY), pp. 169–184.

SIGIR-2005-FergusonSGW #retrieval #scalability #set #using

Top subset retrieval on large collections using sorted indices (PF, AFS, CG, PW), pp. 599–600.

SIGIR-2005-TheobaldSW #incremental #performance #query #self

Efficient and self-tuning incremental query expansion for top-k query processing (MT, RS, GW), pp. 242–249.

VLDB-2004-TheobaldWS #evaluation #probability #query

Top-k Query Evaluation with Probabilistic Guarantees (MT, GW, RS), pp. 648–659.

SAS-2004-NystromKH #analysis #bottom-up #pointer #top-down

Bottom-Up and Top-Down Context-Sensitive Summary-Based Pointer Analysis (EMN, HSK, WmWH), pp. 165–180.

CIKM-2004-ClarkeT #approximate #parallel

Approximating the top-m passages in a parallel question answering system (CLAC, ELT), pp. 454–462.

ECIR-2004-HungWS #bottom-up #clustering #predict #top-down

Predictive Top-Down Knowledge Improves Neural Exploratory Bottom-Up Clustering (CH, SW, PS), pp. 154–166.

ICPR-v1-2004-Hanbury #image #multi

The Morphological Top-Hat Operator Generalised to Multi-Channel Images (AH), pp. 672–675.

DATE-2003-DaglioR #bottom-up #design #top-down

A Fully Qualified Top-Down and Bottom-Up Mixed-Signal Design Flow for Non Volatile Memories Technologies (PD, CR), pp. 20274–20279.

DATE-2003-McCorquodaleGKMSB #challenge #design #top-down

A Top-Down Microsystems Design Methodology and Associated Challenges (MSM, FHG, KLK, EDM, RMS, RBB), pp. 20292–20296.

DATE-2003-RemondB #design #set

Set Top Box SoC Design Methodology at STMicroelectronics (FR, PB), pp. 20220–20223.

ICDAR-2003-IshideraN #case study #generative #image #recognition #top-down #word

A Study on Top-down Word Image Generation for Handwritten Word Recognition (EI, DN), pp. 1173–1177.

SIGMOD-2003-BabcockO #distributed #monitoring

Distributed Top-K Monitoring (BB, CO), pp. 28–39.

VLDB-2003-IlyasAE #database #query #relational

Supporting Top-k Join Queries in Relational Databases (IFI, WGA, AKE), pp. 754–765.

VLDB-2003-XinHLW #bottom-up #integration #named #top-down

Star-Cubing: Computing Iceberg Cubes by Top-Down and Bottom-Up Integration (DX, JH, XL, BWW), pp. 476–487.

VLDB-2003-YuPM #distributed #query

Distributed Top-N Query Processing with Possibly Uncooperative Local Systems (CTY, GP, WM), pp. 117–128.

EDOC-2003-BelaundeBP #component #framework #implementation

Implementing EDOC business components on top of a CCM platform (MB, JB, THP), pp. 208–221.

SIGIR-2003-AgunF #component #hardware #named

HAT: a hardware assisted TOP-DOC inverted index component (SKA, OF), pp. 447–448.

SAC-2003-PenaCA #approach #protocol #top-down

A Top Down Approach for MAS Protocol Descriptions (JP, RC, JLA), pp. 45–49.

DATE-2002-CaiGKO #design #top-down #using

Top-Down System Level Design Methodology Using SpecC, VCC and SystemC (LC, DG, PK, MO), p. 1137.

DATE-2002-SommerRHGMMECSN #design #layout #specification #top-down

From System Specification To Layout: Seamless Top-Down Design Methods for Analog and Mixed-Signal Applications (RS, IRH, EH, UG, PM, FM, KE, CC, PS, GN), pp. 884–891.

SIGMOD-2002-ChangH #query

Minimal probing: supporting expensive predicates for top-k queries (KCCC, SwH), pp. 346–357.

CIKM-2002-LohLAL #analysis #query

Analysis of pre-computed partition top method for range top-k queries in OLAP data cubes (ZXL, TWL, CHA, SYL), pp. 60–67.

SIGIR-2002-WhiteJR #documentation #feedback #ranking #using #web

A system using implicit feedback and top ranking sentences to help users find relevant web documents (RW, JMJ, IR), p. 446.

SIGIR-2002-WhiteRJ #documentation #evaluation #ranking #using

Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes (RW, IR, JMJ), pp. 57–64.

DATE-2001-RioRMPR #design #top-down

Top-down design of a xDSL 14-bit 4MS/s sigma-delta modulator in digital CMOS technology (RdR, JLdlR, FM, MBPV, ÁRV), pp. 348–352.

CIKM-2001-GrabsBS #clustering #database #information retrieval #named

PowerDB-IR — Information Retrieval on Top of a Database Cluster (TG, KB, HJS), pp. 411–418.

CIKM-2001-Karypis #algorithm #evaluation #recommendation

Evaluation of Item-Based Top-N Recommendation Algorithms (GK), pp. 247–254.

KDD-2001-JinTH #database #mining #scalability

Mining top-n local outliers in large databases (WJ, AKHT, JH), pp. 293–298.

IJCAR-2001-Wang #semantics #top-down

A Top-Down Procedure for Disjunctive Well-Founded Semantics (KW), pp. 305–317.

DATE-2000-DubrovaEMM #algorithm #named #optimisation

TOP: An Algorithm for Three-Level Optimization of PLDs (ED, PE, DMM, JCM), p. 751.

ICPR-v2-2000-KimHL #retrieval

Retrieval of the Top N Matches with Support Vector Machines (JJK, BWH, SWL), pp. 2716–2719.

ICDAR-1999-SarkarN

Heeding More Than the Top Template (PS, GN), pp. 382–385.

VLDB-1999-ChaudhuriG #query

Evaluating Top-k Selection Queries (SC, LG), pp. 397–410.

VLDB-1999-DonjerkovicR #optimisation #probability #query

Probabilistic Optimization of Top N Queries (DD, RR), pp. 411–422.

FM-v1-1999-Hoare #bottom-up #programming #top-down

Theories of Programming: Top-Down and Bottom-Up and Meeting in the Middle (CARH), pp. 1–27.

HCI-EI-1999-AarasRH

Can a more neutral position and support of the forearms at the Table top reduce pain for VDU operators. Laboratory and field studies (AA, OR, GH), pp. 51–55.

TOOLS-USA-1999-Lilly #case study #problem #using

Use Case Pitfalls: Top 10 Problems from Real Projects Using Use Cases (SL), pp. 174–183.

DAC-1998-McGrawAK #design #pipes and filters #top-down

A Top-Down Design Environment for Developing Pipelined Datapaths (RMM, JHA, RHK), pp. 236–241.

DATE-1998-VandenbusscheDLGS #design #interface #specification #top-down

Hierarchical Top-Down Design of Analog Sensor Interfaces: From System-Level Specifications Down to Silicon (JV, SD, FL, GGEG, WMCS), pp. 716–720.

ITiCSE-1998-LidtkeZ #approach #collaboration #education #top-down

A top-down, collaborative teaching approach of introductory courses in computer sciences (poster) (DKL, HHZ), p. 291.

ICML-1998-BlockeelRR #clustering #induction #top-down

Top-Down Induction of Clustering Trees (HB, LDR, JR), pp. 55–63.

ICML-1998-RyanP #architecture #composition #learning #named

RL-TOPS: An Architecture for Modularity and Re-Use in Reinforcement Learning (MRKR, MDP), pp. 481–487.

EDTC-1997-LangDG #automation #design #modelling #parametricity #top-down

Automatic transfer of parametric FEM models into CAD-layout formats for top-down design of microsystems (ML, DD, MG), pp. 200–204.

ICFP-1997-AertsV #functional #user interface

A GUI on Top of a Functional Language (KA, KDV), p. 308.

HCI-CC-1997-Majchrzak #design

Software to Support Sociotechnical Design: The Case of Top-Integrator (AM), pp. 229–231.

CADE-1997-HasegawaIOK #bottom-up #proving #set #theorem proving #top-down

Non-Horn Magic Sets to Incorporate Top-down Inference into Bottom-up Theorem Proving (RH, KI, YO, MK), pp. 176–190.

CADE-1997-Iwanuma #proving #theorem proving #top-down

Lemma Matching for a PTTP-based Top-down Theorem Prover (KI), pp. 146–160.

STOC-1996-KearnsM #algorithm #learning #on the #top-down

On the Boosting Ability of Top-Down Decision Tree Learning Algorithms (MJK, YM), pp. 459–468.

FME-1996-ZwiersHLRS #composition #development #reuse #top-down

Modular Completeness: Integrating the Reuse of Specified Software in Top-down Program Development (JZ, UH, YL, WPdR, FAS), pp. 595–608.

IFL-1996-BreitingerKL #concurrent #haskell #implementation

An Implementation of Eden on Top of Concurrent Haskell (SB, UK, RL), pp. 141–161.

ICPR-1996-Pavlidis #bottom-up #challenge #documentation #process #recognition #top-down

Challenges in document recognition bottom up and top down processes (TP), pp. 500–504.

HPDC-1996-Cooperman #c #interface #named #parallel

TOP-C: A Task-Oriented Parallel C Interface (GC), pp. 141–150.

VLDB-1995-HaberIL #named #visualisation

OPOSSUM: Desk-Top Schema Management through Customizable Visualization (EMH, YEI, ML), pp. 527–538.

CAiSE-1995-Pettersson #concept #design #user interface

Designing the User Interface on Top of a Conceptual Model (MP), pp. 231–242.

CIKM-1995-LukasiewiczKKG #approach #constraints #database #nondeterminism #object-oriented #taxonomy

Taxonomic and Uncertain Integrity Constraints in Object-Oriented Databases — the TOP Approach (TL, WK, GK, UG), pp. 241–249.

ILPS-1995-Toman #bottom-up #constraints #datalog #top-down

Top-Down beats Bottom-Up for Constraint Based Extensions of Datalog (DT), pp. 98–112.

VLDB-1994-OuzzaniAB #approach #top-down

A Top-Down Approach for Two Level Serializability (MO, MAA, NLB), pp. 226–237.

CAiSE-1994-LuA #workflow

Building Workflow Applications on Top of WooRKS (GL, MA), pp. 42–52.

CIKM-1994-HeinleinKD #classification #guidelines #representation

Representation of Medical Guidelines on Top of a Classification-Based System (CH, KK, PD), pp. 415–422.

ICML-1994-ZelleMK #bottom-up #induction #logic programming #top-down

Combining Top-down and Bottom-up Techniques in Inductive Logic Programming (JMZ, RJM, JBK), pp. 343–351.

LOPSTR-1994-MarakakisG #data type #design #logic programming #source code #top-down #using

Schema-Based Top-Down Design of Logic Programs Using Abstract Data Types (EIM, JPG), pp. 138–153.

CADE-1994-OhtaniSM

EUODHILOS-II on Top of GNU Epoch (TO, HS, TM), pp. 816–820.

CADE-1994-Schumann #bottom-up #named #preprocessor #proving #theorem proving #top-down

DELTA — A Bottom-up Preprocessor for Top-Down Theorem Provers — System Abstract (JS), pp. 774–777.

ILPS-1994-AlferesDP #named #source code #top-down

SLX — A Top-down Derivation Procedure for Programs with Explicit Negation (JJA, CVD, LMP), pp. 424–438.

PLILP-1993-Nederhof #algorithm #definite clause grammar #parsing #recursion #top-down

A New Top-Down Parsing Algorithm for Left-Recursive DCGs (MJN), pp. 108–122.

VLDB-1992-SimonKM #implementation #relational

Implementing High Level Active Rules on Top of a Relational DBMS (ES, JK, CdM), pp. 315–326.

CC-1992-Muller #parsing #top-down

Attribute-Directed Top-Down Parsing (KM), pp. 37–43.

ECOOP-1991-Champeaux #analysis #development #object-oriented #top-down

Object-Oriented Analysis and Top-Down Software Development (DdC), pp. 360–376.

CAV-1991-GjessingKM #approach #specification #top-down

A Top Down Approach to the Formal Specification of SCI Cache Coherence (SG, SK, EMK), pp. 83–91.

ISLP-1991-RamakrishnanS #bottom-up #revisited #top-down

Top-Down versus Bottom-Up Revisited (RR, SS), pp. 321–336.

ISLP-1991-SatoM #first-order #interpreter #source code #top-down

A Complete Top-Down Interpreter for First Order Programs (TS, FM), pp. 35–53.

ESOP-1990-SchreyePRB #constraints #implementation #logic programming #prolog

Implementing Finite-domain Constraint Logic Programming on Top of a Prolog-System with Delay-mechanism (DDS, DP, JR, MB), pp. 106–117.

CC-1990-Dobler #hybrid #parsing #top-down

A Hybrid Top-Down Parsing Technique (Abstract) (HD), pp. 210–211.

CLP-1990-GriefahnL90 #constraints #database #deduction #top-down

Top-Down Integrity Constraint Checking for Deductive Databases (UG, SL), pp. 130–144.

CLP-1990-LauP90 #first-order #logic #recursion #specification #synthesis #top-down

Top-down Synthesis of Recursive Logic Procedures from First-order Logic Specifications (KKL, SDP), pp. 667–684.

DAC-1989-JabriS #algorithm #knowledge-based #named #top-down

PIAF: A Knowledge-based/Algorithm Top-Down Floorplanning System (MAJ, DJS), pp. 582–585.

PODS-1989-Ullman #bottom-up #datalog #top-down

Bottom-Up Beats Top-Down for Datalog (JDU), pp. 140–149.

ML-1989-BergadanoGP #deduction #induction #learning #top-down

Deduction in Top-Down Inductive Learning (FB, AG, SP), pp. 23–25.

JICSCP-1988-KempT88 #database #evaluation #query #top-down

Completeness of a Top-Down Query Evaluation Procedure for Stratified Databases (DBK, RWT), pp. 178–194.

LICS-1988-Bloom #modelling #λ-calculus

Can LCF Be Topped? Flat Lattice models of Typed λ Calculus (Preliminary Report) (BB), pp. 282–295.

SLP-1985-UedaC85 #compilation #concurrent #prolog

Concurrent Prolog Compiler on Top of Prolog (KU, TC), pp. 119–126.

DAC-1984-KozawaMT #algorithm #layout #logic #top-down

Combine and top down block placement algorithm for hierarchical logic VLSI layout (TK, CM, HT), pp. 667–669.

SIGMOD-1983-Rowe #database #estimation #statistics #top-down

Top-Down Statistical Estimation on a Database (NCR), pp. 135–145.

DAC-1982-AdachiKNS #design #layout #top-down

Hierarchical top-down layout design method for VLSI chip (TA, HK, MN, TS), pp. 785–791.

DAC-1982-BassetS #design #testing #top-down

Top down design and testability of VLSI circuits (PB, GS), pp. 851–857.

ICSE-1978-Lindstrom #parsing #top-down #using

Control Structure Aptness: A Cast Study Using Top-Down Parsing (GL), pp. 5–12.

SIGMOD-1977-Merrett #approach #cost analysis #database #top-down

Database Cost Analysis: a Top-Down Approach (THM), pp. 135–143.

POPL-1973-Pratt #precedence #top-down

Top Down Operator Precedence (VRP), pp. 41–51.

STOC-1969-RosenkrantzS #top-down

Properties of Deterministic Top Down Grammars (DJR, RES), pp. 165–180.