46840073
Better Algorithms for Minimizing Average Flow-Time on Related Machines
We consider the problem of minimising flow time on related machines and give an O(logP)-approximation algorithm for the offline case and an O(log2P)-competitive algorithm for the online version. This improves upon the previous best bound of O(log2P logS) on the competitive ratio. Here P is the ratio of the maximum to the minimum processing time of a job and S is the ratio of the maximum to the minimum speed of a machine.
8488383
Sorting and selection with structured costs
The study of the effect of priced information on basic algorithmic problems was initiated by M. Charikar et al. (2000). The authors continue the study of sorting and selection in the priced comparison model, i.e., when each comparison has an associated cost, and answer some of the open problems suggested by Charikar et al. If the comparison costs are allowed to be arbitrary, we show that one cannot get good approximation ratios. A different way to assign costs is based on the idea that one can distill out an intrinsic value for each item being compared, such that the cost of comparing two elements is some "well-behaved" or "structured" function of their values. We feel that most practical applications will have some structured cost property. The authors study the problems of sorting and selection (which includes finding the maximum and the median) in the structured cost model. We get a variety of approximation results for these problems, depending on the restrictions we put on the structured costs. We show that it is possible to get much improved results with the structured cost model than the case when we do not have any assumptions on comparison costs.
1630993
Join-distinct aggregate estimation over update streams
There is growing interest in algorithms for processing andquerying continuous data streams (i.e., data that is seenonly once in a fixed order) with limited memory resources.Providing (perhaps approximate) answers to queries over suchstreams is a crucial requirement for many application environments;examples include large IP network installations where performancedata from different parts of the network needs to be continuouslycollected and analyzed.
The ability to estimate the number of distinct (sub)tuples inthe result of a join operation correlating two data streams (i.e.,the cardinality of a projection with duplicate elimination over ajoin) is an important requirement for several data-analysisscenarios. For instance, to enable real-time traffic analysis andload balancing, a network-monitoring application may need toestimate the number of distinct (<i>source</i>,destination) IP-address pairs occurring in the stream of IP packetsobserved by router <i>R</i><inf>1</inf>,where the source address is also seen in packets routed through adifferent router <i>R</i><inf>2</inf>.Earlier work has presented solutions to the individual problems ofdistinct counting and join-size estimation (without duplicateelimination) over streams. These solutions, however, arefundamentally different and extending or combining them to handleour more complex "Join-Distinct" estimation problem is far fromobvious. In this paper, we propose the <i>first</i>space-efficient algorithmic solution to the general Join-Distinctestimation problem over continuous data streams (our techniques canactually handle general <i>update streams</i>comprising tuple deletions as well as insertions). Our estimatorsare probabilistic in nature and rely on novel algorithms forbuilding and combining a new class of hash-based synopses (termed"JD <i>sketches</i>") for individual update streams. Wedemonstrate that our algorithms can provide low error,high-confidence Join-Distinct estimates using only small space andsmall processing time per update. In fact, we present lower boundsshowing that the space usage of our estimators is within smallfactors of the best possible for the Join-Distinct problem.Preliminary experimental results verify the effectiveness of ourapproach.
29625431
Building Edge-Failure Resilient Networks
We consider the design of resilient networks that are fault tolerant against single link failures. Resilience against single link failures can be built into the network by providing backup paths, which are used when an edge failure occurs on a primary path. We consider several network design problems in this context: the goal is to provision primary and backup bandwidth while minimizing cost. Our models are motivated by current high speed optical networks and we provide approximation algorithms for the problems below.The main problem we consider is that of backup allocation. In this problem, we are given an already provisioned (primary) network, and we want to reserve backup capacity on the edges of the underlying network so that all the demand can be routed even in the case of a single edge failure. We also consider a variant where the primary network has a tree topology and it is required that the restored network retains the tree topology. We then address the problem of simultaneous primary and backup allocation, in which we are given specifications of the traffic to be handled, and we want to both provision the primary as well as the backup network. We also investigate the online model where the primary network is not known in advance. Demands between source-sink pairs arrive online and the goal is to provide a primary path and set of backup edges that can be used for restoring a failure of any of the primary edges.
5253383
Optimal configuration of OSPF aggregates
Open shortest path first (OSPF) is a popular protocol for routing within an autonomous system (AS) domain. In this paper, we address the important practical problem of configuring OSPF aggregates to minimize the error in OSPF shortest path computations due to subnet aggregation. We first develop an optimal dynamic programming algorithm that, given an upper bound k on the number of aggregates to be advertised and a weight-assignment function for the aggregates, computes the k aggregates that result in the minimum cumulative error in the shortest path computations for all source-destination subnet pairs. Subsequently, we tackle the problem of assigning weights to OSPF aggregates such that the cumulative error in the computed shortest paths is minimized. We demonstrate that, while for certain special cases (e.g., unweighted cumulative error) efficient optimal algorithms for the weight-assignment problem can be devised, the general problem itself is /spl Nscr//spl Pscr/-hard. Consequently, we have to rely on search heuristics to solve the weight-assignment problem. To the best of our knowledge, our work is the first to address the algorithmic issues underlying the configuration of OSPF aggregates and to propose efficient configuration algorithms that are provably optimal for many practical scenarios.
33509544
Multiscale rough set data analysis with application to stock performance modeling
Multiscale Rough Set Data Analysis (MSRSDA) combines the feature selection and feature extraction ability of RSDA with the multiresolution property of the wavelet transform. RSDA removes the irrelevant (having no impact on prediction performance) and dependent (having no discriminatory power) market variables from the multivariate time series data matrix, thereby improving generalization capability of the forecasting engine (neural network, fuzzy inference system). A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Significance of market variables at various wavelet scales representing "detailed" short-term history and "general" long-term history of the time series, is computed using RSDA in wavelet space. Irrelevant and redundant variables at each scale are removed and a forecasting engine is then trained on each of the relevant resolution scales (i.e., those scales where significant events are detected); individual wavelets forecasts are recombined to form the overall forecast. The superior performance of MSRSDA is illustrated through stock performance modeling example. For prediction of S&P500 index, a data matrix of 21 variables has been used. MSRSDA gives 69% accuracy on stock-trend prediction.
18222685
XCHOKe: malicious source control for congestion avoidance at Internet gateways
The paper describes an algorithm to control flows from nonadaptive sources in the Internet in a simple and lightweight fashion. The algorithm, called XCHOKe, is an extension to CHOKe and RED. The paper presents experimental results from simulations that demonstrate that the algorithm outperforms current well-known algorithms for buffer management in providing better protection for TCP flows under varying degrees of attack from non-adaptive flows.
36050355
An efficient algorithm for solving the partial sum query problem
Abstract We address the problem: given n data values, how many partial sums must be stored so that partial sums of any other combination of these n values can be calculated in one arithmetic operation from the stored values. This problem has application in online analytical processing (OLAP) databases [Gray et al., Proc. 12th Int. Conf. Data Eng. (1996)] where it is called the partial sum aggregation query. It can also be used to generate the entire power set of n values efficiently. In this paper, we derive lower bounds for this problem, devise an efficient algorithm to solve it and show that our heuristic comes reasonably close to the lower bound.
21095960
The TRIPLES database: a community resource for yeast molecular biology
TRIPLES is a web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces cerevisiae-a relational database housing nearly half a million data points generated from an ongoing study using large-scale transposon mutagenesis to characterize gene function in yeast. At present, TRIPLES contains three principal data sets (i.e. phenotypic data, protein localization data and expression data) for over 3500 annotated yeast genes as well as several hundred non-annotated open reading frames. In addition, the TRIPLES web site provides online order forms linked to each data set so that users may request any strain or reagent generated from this project free of charge. In response to user requests, the TRIPLES web site has undergone several recent modifications. Our localization data have been supplemented with approximately 500 fluorescent micrographs depicting actual staining patterns observed upon indirect immunofluorescence analysis of indicated epitope-tagged proteins. These localization data, as well as all other data sets within TRIPLES, are now available in full as tab-delimited text. To accommodate increased reagent requests, all orders are now cataloged in a separate database, and users are notified immediately of order receipt and shipment. Also, TRIPLES is one of five sites incorporated into the new functional analysis tool Function Junction provided by the Saccharomyces Genome Database. TRIPLES may be accessed from the Yale Genome Analysis Center (YGAC) homepage at http://ygac.med.yale.edu.
16277175
Oncology Ontology in the NCI Thesaurus
The National Cancer Institute's Thesaurus (NCIT) has been created with the goal of providing a controlled vocabulary which can be used by specialists in the various sub-domains of oncology. It is intended to be used for purposes of annotation in ways designed to ensure the integration of data and information deriving from these various sub-domains, and thus to support more powerful cross-domain inferences. In order to evaluate its suitability for this purpose, we examined the NCIT's treatment of the kinds of entities which are fundamental to an ontology of colon carcinoma. We here describe the problems we uncovered concerning classification, synonymy, relations and definitions, and we draw conclusions for the work needed to establish the NCIT as a reference ontology for the cancer domain in the future.