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- Vijay Arya, Naveen Garg, Rohit Khandekar, Adam Meyerson, Kamesh Munagala, Vinayaka Pandit
- SIAM J. Comput.
- 2001

In this paper, we analyze local search heuristics for the <italic>k</italic>-median and facility location problems. We define the {\em locality gap\/} of a local search procedure as the maximum ratio of a locally optimum solution (obtained using this procedure) to the global optimum. For <italic>k</italic>-median, we show that local search with swaps has a… (More)

- Sudipto Guha, Kamesh Munagala
- ArXiv
- 2007

The celebrated multi-armed bandit problem in decision theory models the central trade-off between exploration, or learning about the state of a system, and exploitation, or utilizing the system. In this paper we study the variant of the multi-armed bandit problem where the exploration phase involves costly experiments and occurs before the exploitation… (More)

Web services are becoming a standard method of sharing data and functionality among loosely-coupled systems. We propose a general-purpose Web Service Management System (WSMS) that enables querying multiple web services in a transparent and integrated fashion. This paper tackles a first basic WSMS problem: query optimization for Select-Project-Join queries… (More)

- Adam Meyerson, Kamesh Munagala, Serge A. Plotkin
- SIAM J. Comput.
- 2000

We present the COST-DISTANCE problem: finding a Steiner tree which optimizes the sum of edge costs along one metric and the sum of source-sink distances along an unrelated second metric. We give the first known £ ¥ ¤ § ¦ © ¨ randomized approximation scheme for COST-DISTANCE, where is the number of sources. We reduce several common network design problems to… (More)

In this paper, we consider the problem of designing incentive compatible auctions for multiple (homogeneous) units of a good, when bidders have private valuations and private budget constraints. When only the valuations are private and the budgets are public, Dobzinski <i>et al</i> [8] show that the <i>adaptive clinching</i> auction is the unique… (More)

- Sudipto Guha, Adam Meyerson, Kamesh Munagala
- J. Algorithms
- 2003

We consider a generalization of the classical facility location problem, where we require the solution to be fault–tolerant. In this generalization, every demand point must be served by Ö facilities instead of just one. The facilities other than the closest one are " backup " facilities for that demand, and any such facility will be used only if all closer… (More)

- Shivnath Babu, Rajeev Motwani, Kamesh Munagala, Itaru Nishizawa, Jennifer Widom
- SIGMOD Conference
- 2004

We consider the problem of <i>pipelined filters</i>, where a continuous stream of tuples is processed by a set of commutative filters. Pipelined filters are common in stream applications and capture a large class of multiway stream joins. We focus on the problem of ordering the filters adaptively to minimize processing cost in an environment where stream… (More)

- Utkarsh Srivastava, Kamesh Munagala, Jennifer Widom
- PODS
- 2005

In sensor networks, data acquisition frequently takes place at low-capability devices. The acquired data is then transmitted through a hierarchy of nodes having progressively increasing network band-width and computational power. We consider the problem of executing queries over these data streams, posed at the root of the hierarchy. To minimize data… (More)

- Adam Silberstein, Rebecca Braynard, Carla Schlatter Ellis, Kamesh Munagala, Jun Yang
- 22nd International Conference on Data Engineering…
- 2006

Wireless sensor networks generate a vast amount of data. This data, however, must be sparingly extracted to conserve energy, usually the most precious resource in battery-powered sensors. When approximation is acceptable, a model-driven approach to query processing is effective in saving energy by avoiding contacting nodes whose values can be predicted or… (More)

- Sudipto Guha, Kamesh Munagala
- STOC
- 2007

We present the first approximation algorithms for a large class of budgeted learning problems. One classicexample of the above is the budgeted multi-armed bandit problem. In this problem each arm of the bandithas an unknown reward distribution on which a prior isspecified as input. The knowledge about the underlying distribution can be refined in the… (More)