Tova Milo, Diego Calvanese
Proceedings of the 34th Symposium on Principles of Database Systems
PODS, 2015.
@proceedings{PODS-2015, acmid = "2745754", address = "Victoria, Australia", editor = "Tova Milo and Diego Calvanese", isbn = "978-1-4503-2757-2", publisher = "{ACM}", title = "{Proceedings of the 34th Symposium on Principles of Database Systems}", year = 2015, }
Contents (29 items)
- PODS-2015-Jordan #big data
- Computational Thinking, Inferential Thinking and “Big Data” (MIJ), p. 1.
- PODS-2015-FaginKK #complexity
- Dichotomies in the Complexity of Preferred Repairs (RF, BK, PGK), pp. 3–15.
- PODS-2015-KoutrisW #complexity #consistency #constraints #query #self
- The Data Complexity of Consistent Query Answering for Self-Join-Free Conjunctive Queries Under Primary Key Constraints (PK, JW), pp. 17–29.
- PODS-2015-CateCST #ontology #using
- High-Level Why-Not Explanations using Ontologies (BtC, CC, ES, WCT), pp. 31–43.
- PODS-2015-AmelootGKNS #query
- Parallel-Correctness and Transferability for Conjunctive Queries (TJA, GG, BK, FN, TS), pp. 47–58.
- PODS-2015-Green #declarative #enterprise #named
- LogiQL: A Declarative Language for Enterprise Applications (TJG), pp. 59–64.
- PODS-2015-GottlobPS #dependence
- Function Symbols in Tuple-Generating Dependencies: Expressive Power and Computability (GG, RP, ES), pp. 65–77.
- PODS-2015-AlvianoP
- Default Negation for Non-Guarded Existential Rules (MA, AP), pp. 79–90.
- PODS-2015-CalauttiGP #termination
- Chase Termination for Guarded Existential Rules (MC, GG, AP), pp. 91–103.
- PODS-2015-GrahneMO
- Recovering Exchanged Data (GG, AM, AO), pp. 105–116.
- PODS-2015-CzerwinskiMPP
- The (Almost) Complete Guide to Tree Pattern Containment (WC, WM, PP, MP), pp. 117–130.
- PODS-2015-BarceloPS #approximate #evaluation #performance
- Efficient Evaluation and Approximation of Well-designed Pattern Trees (PB, RP, SS), pp. 131–144.
- PODS-2015-MartensNNS #named #xml
- BonXai: Combining the simplicity of DTD with the expressiveness of XML Schema (WM, FN, MN, TS), pp. 145–156.
- PODS-2015-Cormode #dataset #scalability #summary
- Compact Summaries over Large Datasets (GC), pp. 157–158.
- PODS-2015-PraveenS #graph #how #question
- Defining Relations on Graphs: How Hard is it in the Presence of Node Partitions? (MP, BS), pp. 159–172.
- PODS-2015-FanGCDL #big data #query
- Querying Big Data by Accessing Small Data (WF, FG, YC, TD, PL), pp. 173–184.
- PODS-2015-GrozM #query
- Skyline Queries with Noisy Comparisons (BG, TM), pp. 185–198.
- PODS-2015-GuchtWWZ #communication #complexity #distributed #matrix #multi
- The Communication Complexity of Distributed Set-Joins with Applications to Matrix Multiplication (DVG, RW, DPW, QZ), pp. 199–212.
- PODS-2015-KhamisNRR #geometry #worst-case
- Joins via Geometric Resolutions: Worst-case and Beyond (MAK, HQN, CR, AR), pp. 213–228.
- PODS-2015-HuQT #memory management
- External Memory Stream Sampling (XH, MQ, YT), pp. 229–239.
- PODS-2015-GuhaMT #graph
- Vertex and Hyperedge Connectivity in Dynamic Graph Streams (SG, AM, DT), pp. 241–247.
- PODS-2015-AcharyaDHLS #algorithm #approximate #performance
- Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms (JA, ID, CH, JZL, LS), pp. 249–263.
- PODS-2015-RahulT #2d #on the
- On Top-k Range Reporting in 2D Space (SR, YT), pp. 265–275.
- PODS-2015-MunroNV #data type #documentation #graph
- Dynamic Data Structures for Document Collections and Graphs (JIM, YN, JSV), pp. 277–289.
- PODS-2015-HuQT15a #dependence #testing
- Join Dependency Testing, Loomis-Whitney Join, and Triangle Enumeration (XH, MQ, YT), pp. 291–301.
- PODS-2015-Ullman #linear #multi #query
- Private Multiplicative Weights Beyond Linear Queries (JU), pp. 303–312.
- PODS-2015-BeameBGS #first-order #symmetry
- Symmetric Weighted First-Order Model Counting (PB, GVdB, EG, DS), pp. 313–328.
- PODS-2015-Kapralov #complexity #nearest neighbour #query #trade-off
- Smooth Tradeoffs between Insert and Query Complexity in Nearest Neighbor Search (MK), pp. 329–342.
- PODS-2015-IndykLR #approximate #testing
- Erratum for: Approximating and Testing k-Histogram Distributions in Sub-linear Time (PI, RL, RR), p. 343.
6 ×#query
4 ×#complexity
3 ×#approximate
3 ×#graph
2 ×#big data
2 ×#dependence
2 ×#multi
2 ×#named
2 ×#performance
2 ×#testing
4 ×#complexity
3 ×#approximate
3 ×#graph
2 ×#big data
2 ×#dependence
2 ×#multi
2 ×#named
2 ×#performance
2 ×#testing