Results 1  10
of
29
What’s hot and what’s not: Tracking most frequent items dynamically
 In Proceedings of ACM Principles of Database Systems
, 2003
"... Most database management systems maintain statistics on the underlying relation. One of the important statistics is that of the “hot items ” in the relation: those that appear many times (most frequently, or more than some threshold). For example, endbiased histograms keep the hot items as part of ..."
Abstract

Cited by 174 (14 self)
 Add to MetaCart
Most database management systems maintain statistics on the underlying relation. One of the important statistics is that of the “hot items ” in the relation: those that appear many times (most frequently, or more than some threshold). For example, endbiased histograms keep the hot items as part of the histogram and are used in selectivity estimation. Hot items are used as simple outliers in data mining, and in anomaly detection in many applications. We present new methods for dynamically determining the hot items at any time in a relation which is undergoing deletion operations as well as inserts. Our methods maintain small space data structures that monitor the transactions on the relation, and when required, quickly output all hot items, without rescanning the relation in the database. With userspecified probability, all hot items are correctly reported. Our methods rely on ideas from “group testing”. They are simple to implement, and have provable quality, space and time guarantees. Previously known algorithms for this problem that make similar quality and performance guarantees can not handle deletions, and those that handle deletions can not make similar guarantees without rescanning the database. Our experiments with real and synthetic data show that our algorithms are accurate in dynamically tracking the hot items independent of the rate of insertions and deletions.
What's New: Finding Significant Differences in Network Data Streams
 in Proc. of IEEE Infocom
, 2004
"... Monitoring and analyzing network traffic usage patterns is vital for managing IP Networks. An important problem is to provide network managers with information about changes in traffic, informing them about "what's new". Specifically, we focus on the challenge of finding significantly large differen ..."
Abstract

Cited by 67 (8 self)
 Add to MetaCart
Monitoring and analyzing network traffic usage patterns is vital for managing IP Networks. An important problem is to provide network managers with information about changes in traffic, informing them about "what's new". Specifically, we focus on the challenge of finding significantly large differences in traffic: over time, between interfaces and between routers. We introduce the idea of a deltoid: an item that has a large difference, whether the difference is absolute, relative or variational. We present novel...
Combinatorial Algorithms for Compressed Sensing
 In Proc. of SIROCCO
, 2006
"... Abstract — In sparse approximation theory, the fundamental problem is to reconstruct a signal A ∈ R n from linear measurements 〈A, ψi 〉 with respect to a dictionary of ψi’s. Recently, there is focus on the novel direction of Compressed Sensing [1] where the reconstruction can be done with very few—O ..."
Abstract

Cited by 62 (1 self)
 Add to MetaCart
Abstract — In sparse approximation theory, the fundamental problem is to reconstruct a signal A ∈ R n from linear measurements 〈A, ψi 〉 with respect to a dictionary of ψi’s. Recently, there is focus on the novel direction of Compressed Sensing [1] where the reconstruction can be done with very few—O(k log n)— linear measurements over a modified dictionary if the signal is compressible, that is, its information is concentrated in k coefficients with the original dictionary. In particular, these results [1], [2], [3] prove that there exists a single O(k log n) × n measurement matrix such that any such signal can be reconstructed from these measurements, with error at most O(1) times the worst case error for the class of such signals. Compressed sensing has generated tremendous excitement both because of the sophisticated underlying Mathematics and because of its potential applications. In this paper, we address outstanding open problems in Compressed Sensing. Our main result is an explicit construction of a nonadaptive measurement matrix and the corresponding reconstruction algorithm so that with a number of measurements polynomial in k, log n, 1/ε, we can reconstruct compressible signals. This is the first known polynomial time explicit construction of any such measurement matrix. In addition, our result improves the error guarantee from O(1) to 1 + ε and improves the reconstruction time from poly(n) to poly(k log n). Our second result is a randomized construction of O(k polylog(n)) measurements that work for each signal with high probability and gives perinstance approximation guarantees rather than over the class of all signals. Previous work on Compressed Sensing does not provide such perinstance approximation guarantees; our result improves the best known number of measurements known from prior work in other areas including Learning Theory [4], [5], Streaming algorithms [6], [7], [8] and Complexity Theory [9] for this case. Our approach is combinatorial. In particular, we use two parallel sets of group tests, one to filter and the other to certify and estimate; the resulting algorithms are quite simple to implement. I.
Efficient tracing of failed nodes in sensor networks
 In Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications
, 2002
"... This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyrig ..."
Abstract

Cited by 33 (0 self)
 Add to MetaCart
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Copyright c○2002 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior
Defect Tolerance at the End of the Roadmap
 IN ITC
, 2004
"... Defect tolerance will become more important as feature sizes shrink closer to single digit nanometer dimensions. This is true whether the chips are manufactured using topdown methods (e.g., photolithography) or bottomup methods (e.g., chemically assembled electronic nanotechnology, or CAEN). In thi ..."
Abstract

Cited by 29 (1 self)
 Add to MetaCart
Defect tolerance will become more important as feature sizes shrink closer to single digit nanometer dimensions. This is true whether the chips are manufactured using topdown methods (e.g., photolithography) or bottomup methods (e.g., chemically assembled electronic nanotechnology, or CAEN). In this paper, we propose a defect tolerance methodology centered around reconfigurable devices, a scalable testing method, and dynamic placeandroute. Our methodology is particularly well suited for CAEN.
Towards an algorithmic theory of compressed sensing
, 2005
"... In Approximation Theory, the fundamental problem is to reconstruct a signal A ∈ Rn from linear measurements 〈A, ψi 〉 with respect to a dictionary Ψ for Rn. Recently, there has been tremendous excitement about the novel direction of Compressed Sensing [10] where the reconstruction can be done with ve ..."
Abstract

Cited by 25 (1 self)
 Add to MetaCart
In Approximation Theory, the fundamental problem is to reconstruct a signal A ∈ Rn from linear measurements 〈A, ψi 〉 with respect to a dictionary Ψ for Rn. Recently, there has been tremendous excitement about the novel direction of Compressed Sensing [10] where the reconstruction can be done with very few — Õ(k)—linear measurements over a modified dictionary Ψ ′ if the information of the signal is concentrated in k coefficients over an orthonormal basis Ψ. These results have reconstruction error on any given signal that is optimal with respect to a broad class of signals. In a series of papers and meetings over the past year, a theory of Compressed Sensing has been developed by mathematicians. We develop an algorithmic perspective for the Compressed Sensing problem, showing that Compressed Sensing results resonate with prior work in Group Testing, Learning theory and Streaming algorithms. Our main contributions are new algorithms that present the most general results for Compressed Sensing with 1 + ɛ approximation on every signal, faster The dictionary Ψ denotes an orthonormal basis for Rn, i.e. Ψ is a set of n realvalued vectors
DESIGNING COMPRESSIVE SENSING DNA MICROARRAYS
"... A Compressive Sensing Microarray (CSM) is a new device for DNAbased identification of target organisms that leverages the nascent theory of Compressive Sensing (CS). In contrast to a conventional DNA microarray, in which each genetic sensor spot is designed to respond to a single target organism, i ..."
Abstract

Cited by 14 (3 self)
 Add to MetaCart
A Compressive Sensing Microarray (CSM) is a new device for DNAbased identification of target organisms that leverages the nascent theory of Compressive Sensing (CS). In contrast to a conventional DNA microarray, in which each genetic sensor spot is designed to respond to a single target organism, in a CSM each sensor spot responds to a group of targets. As a result, significantly fewer total sensor spots are required. In this paper, we study how to design group identifier probes that simultaneously account for both the constraints from the CS theory and the biochemistry of probetarget DNA hybridization. We employ Belief Propagation as a CS recovery method to estimate target concentrations from the microarray intensities.
Combinatorial search on graphs motivated by bioinformatics applications: A brief survey
 WG 2005. LNCS
, 2005
"... Abstract. The goal of this paper is to present a brief survey of a collection of methods and results from the area of combinatorial search [1,8] focusing on graph reconstruction using queries of different type. The study is motivated by applications to genome sequencing. ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
Abstract. The goal of this paper is to present a brief survey of a collection of methods and results from the area of combinatorial search [1,8] focusing on graph reconstruction using queries of different type. The study is motivated by applications to genome sequencing.
Finding Popular Categories for RFID Tags
, 2008
"... As RFID tags are increasingly attached to everyday items, it quickly becomes impractical to collect data from every tag in order to extract useful information. In this paper, we consider the problem of identifying popular categories of RFID tags out of a large collection of tags, without reading all ..."
Abstract

Cited by 11 (6 self)
 Add to MetaCart
As RFID tags are increasingly attached to everyday items, it quickly becomes impractical to collect data from every tag in order to extract useful information. In this paper, we consider the problem of identifying popular categories of RFID tags out of a large collection of tags, without reading all the tag data. We propose two algorithms based on the idea of group testing, which allows us to efficiently derive popular categories of tags. We evaluate our solutions using both theoretical analysis and simulation.
Expediting GABased Evolution Using Group Testing Techniques for Reconfigurable Hardware
 in proc. International Conference on Reconfigurable Computing and FPGAs (Reconfig'06), San Luis Potosi
, 2006
"... Autonomous repair and refurbishment of reprogrammable logic devices using Genetic Algorithms can improve the fault tolerance of remote missioncritical systems. The goal of increasing availability by minimizing the repair time is addressed in this paper using a CGTpruned Genetic Algorithm. The propo ..."
Abstract

Cited by 8 (8 self)
 Add to MetaCart
Autonomous repair and refurbishment of reprogrammable logic devices using Genetic Algorithms can improve the fault tolerance of remote missioncritical systems. The goal of increasing availability by minimizing the repair time is addressed in this paper using a CGTpruned Genetic Algorithm. The proposed method utilizes resource performance information obtained using Combinatorial Group Testing (CGT) techniques to evolve refurbished configurations in fewer generations than conventional genetic algorithms. A 3bit x 2bit Multiplier circuit was evolved using both conventional and CGTpruned genetic algorithms. Results show that the new approach yields completely refurbished configurations 37.6 % faster than conventional genetic algorithms. In addition it is demonstrated that for the same circuit, refurbishment of partiallyfunctional configurations is a more tractable problem than designing the configurations when using genetic algorithms as results show the former to take 80 % fewer generations. 1.