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Workflow Mining: Discovering process models from event logs
 IEEE Transactions on Knowledge and Data Engineering
, 2003
"... Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated timeconsuming process and typically there are discrepancies between the ac ..."
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Cited by 400 (57 self)
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Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated timeconsuming process and typically there are discrepancies between
StateSpace Synthesis of CurrentMode FirstOrder LogDomain Filters
"... This paper proposes currentmode firstorder logdomain filters, which are systematically derived using the statespace synthesis procedure. Firstorder lowpass, highpass, and allpass responses are obtained with different circuit types. The filter circuits have very simple structures, since they ..."
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This paper proposes currentmode firstorder logdomain filters, which are systematically derived using the statespace synthesis procedure. Firstorder lowpass, highpass, and allpass responses are obtained with different circuit types. The filter circuits have very simple structures, since
A SecondOrder LogDomain Bandpass Filter for Audio Frequency Applications
 International Symposium on Circuits and Systems (ISCAS
, 1998
"... Abstract — Logdomain filters have recently come into the limelight of the VLSI community as an important class of circuits for implementing continuoustime filters in the current domain [4], [3], [2]. Some papers have discussed logdomain circuit analysis [1] and some have addressed circuit synthes ..."
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Cited by 3 (1 self)
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Abstract — Logdomain filters have recently come into the limelight of the VLSI community as an important class of circuits for implementing continuoustime filters in the current domain [4], [3], [2]. Some papers have discussed logdomain circuit analysis [1] and some have addressed circuit
Higher Order LogConcavity in Euler's Difference Table
"... Let ek n be the entries in the classical Euler's difference table. We consider the array dk n = ek ..."
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Cited by 1 (0 self)
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Let ek n be the entries in the classical Euler's difference table. We consider the array dk n = ek
HiLog: A foundation for higherorder logic programming
 JOURNAL OF LOGIC PROGRAMMING
, 1993
"... We describe a novel logic, called HiLog, and show that it provides a more suitable basis for logic programming than does traditional predicate logic. HiLog has a higherorder syntax and allows arbitrary terms to appear in places where predicates, functions and atomic formulas occur in predicate calc ..."
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Cited by 252 (42 self)
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We describe a novel logic, called HiLog, and show that it provides a more suitable basis for logic programming than does traditional predicate logic. HiLog has a higherorder syntax and allows arbitrary terms to appear in places where predicates, functions and atomic formulas occur in predicate
The Average Distance in a Random Graph with Given Expected Degrees
"... Random graph theory is used to examine the “smallworld phenomenon”– any two strangers are connected through a short chain of mutual acquaintances. We will show that for certain families of random graphs with given expected degrees, the average distance is almost surely of order log n / log ˜ d whe ..."
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Cited by 289 (13 self)
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Random graph theory is used to examine the “smallworld phenomenon”– any two strangers are connected through a short chain of mutual acquaintances. We will show that for certain families of random graphs with given expected degrees, the average distance is almost surely of order log n / log ˜ d
How to Use Expert Advice
 JOURNAL OF THE ASSOCIATION FOR COMPUTING MACHINERY
, 1997
"... We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worstcase situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance of the ..."
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Cited by 377 (79 self)
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is on the order of the square root of the number of mistakes of the best expert, and we give efficient algorithms that achieve this. Our upper and lower bounds have matching leading constants in most cases. We then show howthis leads to certain kinds of pattern recognition/learning algorithms with performance
The Power of Convex Relaxation: NearOptimal Matrix Completion
, 2009
"... This paper is concerned with the problem of recovering an unknown matrix from a small fraction of its entries. This is known as the matrix completion problem, and comes up in a great number of applications, including the famous Netflix Prize and other similar questions in collaborative filtering. In ..."
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Cited by 359 (7 self)
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is on the order of the information theoretic limit (up to logarithmic factors). This convex program simply finds, among all matrices consistent with the observed entries, that with minimum nuclear norm. As an example, we show that on the order of nr log(n) samples are needed to recover a random n × n matrix
Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2008
"... We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added ℓ1norm penalty term. The problem as formulated is convex but the memor ..."
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Cited by 334 (2 self)
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be interpreted as recursive ℓ1norm penalized regression. Our second algorithm, based on Nesterov’s first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright
Random sampling with a reservoir
 ACM Transactions on Mathematical Software
, 1985
"... We introduce fast algorithms for selecting a random sample of n records without replacement from a pool of N records, where the value of N is unknown beforehand. The main result of the paper is the design and analysis of Algorithm Z; it does the sampling in one pass using constant space and in O(n(1 ..."
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Cited by 335 (2 self)
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(n(1 + log(N/n))) expected time, which is optimum, up to a constant factor. Several optimizations are studied that collectively improve the speed of the naive version of the algorithm by an order of magnitude. We give an efficient Pascallike implementation that incorporates these modifications
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