Property testing and its connection to learning and approximation
Journal of the ACM (JACM)
Online computation and competitive analysis
Online computation and competitive analysis
Adaptive set intersections, unions, and differences
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Algorithms
SIAM Journal on Discrete Mathematics
A Lower Bound for Testing 3-Colorability in Bounded-Degree Graphs
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Supporting Incremental Join Queries on Ranked Inputs
Proceedings of the 27th International Conference on Very Large Data Bases
Finding hidden independent sets in interval graphs
Theoretical Computer Science
SIAM Journal on Computing
IEEE Transactions on Knowledge and Data Engineering
Fast Approximate Similarity Search in Extremely High-Dimensional Data Sets
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Finding k-dominant skylines in high dimensional space
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Survey of graph database models
ACM Computing Surveys (CSUR)
A Characterization of the (Natural) Graph Properties Testable with One-Sided Error
SIAM Journal on Computing
Learning a hidden graph using O( logn) queries per edge
Journal of Computer and System Sciences
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Evaluating rank joins with optimal cost
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Probabilistic top-k and ranking-aggregate queries
ACM Transactions on Database Systems (TODS)
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Every Monotone Graph Property Is Testable
SIAM Journal on Computing
The geometry of binary search trees
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Multi-dimensional top-k dominating queries
The VLDB Journal — The International Journal on Very Large Data Bases
Instance-Optimal Geometric Algorithms
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Progressive processing of subspace dominating queries
The VLDB Journal — The International Journal on Very Large Data Bases
Exact and approximate algorithms for the most connected vertex problem
ACM Transactions on Database Systems (TODS)
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An (edge) hidden graph is a graph whose edges are not explicitly given. Detecting the presence of an edge requires expensive edge-probing queries. We consider the k most connected vertex problem on hidden bipartite graphs. Specifically, given a bipartite graph G with independent vertex sets B and W, the goal is to find the k vertices in B with the largest degrees using the minimum number of queries. This problem can be regarded as a top-k extension of a semi-join, and is encountered in many applications in practice (e.g., top-k spatial join with arbitrarily complex join predicates). If B and W have n and m vertices respectively, the number of queries needed to solve the problem is nm in the worst case. This, however, is a pessimistic estimate on how many queries are necessary on practical data. In fact, on some easy inputs, the problem can be efficiently settled with only km+ n edges, which is significantly lower than nm for k n. The huge difference between km + n and nm makes it interesting to design an adaptive algorithm that is guaranteed to achieve the best possible performance on every input G. We give such an algorithm, and prove that it is instance optimal among a broad class of solutions. This means that, for any G, our algorithm can perform more queries than the optimal solution (which is currently unknown) by only a constant factor, which can be shown to be at most 2. Extensive experiments demonstrate that, in practice, the number of queries required by our technique is far less than nm, and agrees with our theoretical findings very well.