About: Communication complexity is a(n) research topic. Over the lifetime, 3870 publication(s) have been published within this topic receiving 105832 citation(s).
Papers published on a yearly basis
TL;DR: Several properties of the graph-theoretic complexity are proved which show, for example, that complexity is independent of physical size and complexity depends only on the decision structure of a program.
Abstract: This paper describes a graph-theoretic complexity measure and illustrates how it can be used to manage and control program complexity. The paper first explains how the graph-theory concepts apply and gives an intuitive explanation of the graph concepts in programming terms. The control graphs of several actual Fortran programs are then presented to illustrate the correlation between intuitive complexity and the graph-theoretic complexity. Several properties of the graph-theoretic complexity are then proved which show, for example, that complexity is independent of physical size (adding or subtracting functional statements leaves complexity unchanged) and complexity depends only on the decision structure of a program.
•01 Jan 1996
TL;DR: This chapter surveys the theory of two-party communication complexity and presents results regarding the following models of computation: • Finite automata • Turing machines • Decision trees • Ordered binary decision diagrams • VLSI chips • Networks of threshold gates.
Abstract: In this chapter we survey the theory of two-party communication complexity. This field of theoretical computer science aims at studying the following, seemingly very simple, scenario: There are two players Alice who holds an n-bit string x and Bob who holds an n-bit string y. Their goal is to communicate in order to compute the value of some boolean function f(x, y), while exchanging a number of bits which is as small as possible. In the first part of this survey we present, mainly by giving examples, some of the results (and techniques) developed as part of this theory. We put an emphasis on proving lower bounds on the amount of communication that must be exchanged in the above scenario for certain functions f . In the second part of this survey we will exemplify the wide applicability of the results proved in the first part to other areas of computer science. While it is obvious that there are many applications of the results to problems in which communication is involved (e.g., in distributed systems), we concentrate on applications in which communication does not appear explicitly in the statement of the problems. In particular, we present results regarding the following models of computation: • Finite automata • Turing machines • Decision trees • Ordered binary decision diagrams (OBDDs) • VLSI chips • Networks of threshold gates We provide references to many other issues and applications of communication complexity which are not discussed in this survey.
TL;DR: Data Streams: Algorithms and Applications surveys the emerging area of algorithms for processing data streams and associated applications, which rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity.
Abstract: In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges. This article is an overview and survey of data stream algorithmics and is an updated version of .
••23 Oct 1995
TL;DR: Schemes that enable a user to access k replicated copies of a database and privately retrieve information stored in the database and get no information on the identity of the item retrieved by the user are described.
Abstract: We describe schemes that enable a user to access k replicated copies of a database (k/spl ges/2) and privately retrieve information stored in the database. This means that each individual database gets no information on the identity of the item retrieved by the user. For a single database, achieving this type of privacy requires communicating the whole database, or n bits (where n is the number of bits in the database). Our schemes use the replication to gain substantial saving. In particular, we have: A two database scheme with communication complexity of O(n/sup 1/3/). A scheme for a constant number, k, of databases with communication complexity O(n/sup 1/k/). A scheme for 1/3 log/sub 2/ n databases with polylogarithmic (in n) communication complexity.
••01 Dec 2007
TL;DR: A continuous-time distributed Kalman filter that uses local aggregation of the sensor data but attempts to reach a consensus on estimates with other nodes in the network and gives rise to two iterative distributedKalman filtering algorithms with different consensus strategies on estimates.
Abstract: In this paper, we introduce three novel distributed Kalman filtering (DKF) algorithms for sensor networks. The first algorithm is a modification of a previous DKF algorithm presented by the author in CDC-ECC '05. The previous algorithm was only applicable to sensors with identical observation matrices which meant the process had to be observable by every sensor. The modified DKF algorithm uses two identical consensus filters for fusion of the sensor data and covariance information and is applicable to sensor networks with different observation matrices. This enables the sensor network to act as a collective observer for the processes occurring in an environment. Then, we introduce a continuous-time distributed Kalman filter that uses local aggregation of the sensor data but attempts to reach a consensus on estimates with other nodes in the network. This peer-to-peer distributed estimation method gives rise to two iterative distributed Kalman filtering algorithms with different consensus strategies on estimates. Communication complexity and packet-loss issues are discussed. The performance and effectiveness of these distributed Kalman filtering algorithms are compared and demonstrated on a target tracking task.
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