Process Dependency Graph Research Articles

Integrating run‐time changes into system and software process enactment

AbstractIn System and Software Engineering development, unforeseen changes occurring during process enactment are almost inevitable but often poorly managed due to a lack of efficient mechanisms for spontaneously handling these run‐time changes. We proposed a change‐aware process management system that allows process actors reporting emergent changes, analyzing possible impacts, and notifying people affected by the changes. To this end, we integrated a Change Management Component with a Process Management System. The Process Management System monitors process enactment and uses the run‐time process information to construct a Process dependency graph (PDG) representing the dependencies among the elements of running processes. The Change Management Component captures change requests sent asynchronously, then reasons the PDG to determine impacted elements. Our PDG reflects the information of process instances and therefore can uncover the intra‐process or inter‐processes dependencies that are invisible on process models. We implemented a prototype named CAPE based on the platform jBPM and the graph database Neo4j. Copyright © 2016 John Wiley & Sons, Ltd.

Decentralized Communication for Data Dependency Analysis Among Process Execution Agents

This paper presents the authors results with the investigation of decentralized data dependency analysis among concurrently executing processes in a service-oriented environment. Distributed Process Execution Agents (PEXAs) are responsible for controlling the execution of processes that are composed of web services. PEXAs are also associated with specific distributed sites for the purpose of capturing data changes that occur at those sites in the context of service executions using Delta-Enabled Grid Services. PEXAs then exchange this information with other PEXAs to dynamically discover data dependencies that can be used to enhance recovery activities for concurrent processes that execute with relaxed isolation properties. This paper outlines the functionality of PEXAs, describing the data structures, algorithms, and communication mechanisms that are used to support decentralized construction of distributed process dependency graphs, demonstrating a more dynamic and intelligent approach to identifying how the failure of one process can potentially affect other concurrently executing processes.

Development of Distance Measures for Process Mining, Discovery and Integration

Business processes continue to play an important role in today’s service-oriented enterprise computing systems. Mining, discovering, and integrating process-oriented services has attracted growing attention in the recent years. In this article, we present a quantitative approach to modeling and capturing the similarity and dissimilarity between different process designs. We derive the similarity measures by analyzing the process dependency graphs of the participating workflow processes. We first convert each process dependency graph into a normalized process matrix. Then we calculate the metric space distance between the normalized matrices. This distance measure can be used as a quantitative and qualitative tool in process mining, process merging, and process clustering, and ultimately it can reduce or minimize the costs involved in design, analysis, and evolution of workflow systems.

Systolic arrays for integer Chinese remaindering

This paper presents time-minimal and processor-time-minimal systolic arrays for computing a process dependence graph corresponding to the mixed-radix conversion algorithm. The arrays are particularly suitable for implementation of algorithms from the applications of residue number systems on programmable systolic/wavefront arrays and in VLSI. Examples of such applications are exact solution of linear equations and computation of pseudo-inverses of matrices.

Bit-level systolic arrays for modular multiplication

This paper presents bit-level cellular arrays implementing Blakley's algorithm for multiplication of twon-bit integers modulo anothern-bit integer. The semi-systolic version uses 3n(n+3) single-bit carry save adders and 2n copies of 3-bit carry look-ahead logic, and computes a pair of binary numbers (C, S) in 3n clock cycles such thatC+S?[0, 2N). The carry look-ahead logic is used to estimate the sign of the partial product, which is needed during the reduction process. The final result in the correct range [0,N) can easily be obtained by computingC+S andC+S?N, and selecting the latter if it is positive; otherwise, the former is selected. We construct a localized process dependence graph of this algorithm, and introduce a systolic array containing 3nw simple adder cells. The latency of the systolic array is 6n+w?2, wherew=?n/2?. The systolic version does not require broadcast and can be used to efficiently compute several modular multiplications in a pipelined fashion, producing a result in every clock cycle.

Decomposing polynomial interpolation for systolic arrays

This paper (1) summarizes recent work on systolic computation of interpolating polynomials, presenting several time-optimal and spacetime-optimal systolic arrays for computing a process dependence graph for Aitken's algorithm, and (2) presents an efficient decomposition of the computation by partitioning the corresponding so-called Newton-Vandermonde matrix. Such a decomposition is used to treat an interpolation problem whose size exceeds the array size.

Systolic computation of interpolating polynomials

Several time-optimal and spacetime-optimal systolic arrays are presented for computing a process dependence graph corresponding to the Aitken algorithm. It is shown that these arrays also can be used to compute the generalized divided differences, i.e., the coefficients of the Hermite interpolating polynomial. Multivariate generalized divided differences are shown to be efficiently computed on a 2-dimensional systolic array. The techniques also are applied to the Neville algorithm, producing similar results.

Systolic computation of multivariable frequency response

An algorithm intended for software implementation on a programmable systolic/wavefront computer is presented for the computation of a complex-valued frequency-response matrix G. Typically, real-valued state-space model matrices are given and the calculation of G must be performed for a very large number of values of the scalar frequency parameter. The algorithm is an orthogonal version of an algorithm described previously by A.J. Laub (ibid., vol.26, no.4, p.407-8, 1981). The system matrix A is reduced initially to an upper Hessenberg form which is preserved as the frequency varies subsequently. A systolic QR factorization of a certain complex-valued matrix is then implemented for effecting the necessary linear system solution (inversion). The critical computational component is the back solve. This computational component's process dependency graph is embedded optimally in space and time through the use of a nonlinear spacetime transformation. The computational period of the algorithm is O(n) where n is the order of the matrix A. >