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Discovering and optimising processes from event logs of document management systems

100%

Mędrek M. , Wiechetek Ł.

Organizacja i Zarządzanie : kwartalnik naukowy

2017

tom nr 3

69--82

Modern information management systems are often driven by workflow engines, which require an accurate, detailed, and structured process description. Instead of theoretical modelling of such processes we propose a technique which can be a connection between traditional management (organisational structure) and business process management approach. In our work we present a case study where the automatic process mining techniques are applied to administrative processes in an environment of event logs recorded by DMS. We use event log data to discover and describe the process map of the organisation and to model structured process descriptions. Our approach allows for the extraction of models based on facts and it is a direct connection between the process model and raw data generated by the organisation. Such scenario increases consistency between the mapped model and the reality of the organisation.

Algorytm budowy reprezentacji przedziałowej grafu jako heurystyka dla problemu mapowania DNA

100%

Bogdanowicz D.

Zeszyty Naukowe Wydziału ETI Politechniki Gdańskiej. Technologie Informacyjne

2007

tom T. 12

109-116

W pracy dokonano analizy przydatności algorytmu Corneil'a budowy reprezentacji przedziałowej grafu jako heurystyki dla problemu tworzenia map fizycznych DNA. Prezentowana analiza dotyczy dwóch osobno rozpatrywanych przypadków, w których do danych wzorcowych wprowadzamy odpowiednio błędy negatywne (reprezentujące niedobór informacji) oraz błędy pozytywne (reprezentujące fałszywe informacje). Rozpatrywany algorytm zachowuje się znacznie lepiej w przypadku obecności wyłącznie błędów negatywnych.

The article contains an usability analysis of algorithm to build interval representation of graphs described by Corneil as a heuristic in DNA physical mapping problem. Described analysis consist of two separate cases: only false negative and only false positive errors. The algorithm deals much better with presence only false negatives errors.

Application of grammatical evolution to stock price prediction

80%

Kita E. , Sugiura H. , Zuo Y. , Mizuno T.

Computer Assisted Methods in Engineering and Science

2017

tom Vol. 24, no. 1

67--81

Grammatical evolution (GE) is one of evolutionary computation techniques. The aim of GE is to find the function or the executable program or program fragment that will find the optimal solution for the design objective such as the function for representing the set of given data, the robot control algorithm and so on. Candidate solutions are described in bit string. The mapping process from the genotype (bitstring) to the phenotype (function or program or program fragment) is defined according to the list of production rules of terminal and non-terminal symbols. Candidate solutions are evolved according to the search algorithm based on genetic algorithm (GA). There are three main issues in GE: genotype definition, production rules, and search algorithm. Grammatical evolution with multiple chromosomes (GEMC) is one of the improved algorithms of GE. In GEMC, the convergence property of GE is improved by modifying the genotype definition. The aim of this study is to improve convergence property by changing the search algorithm based on GA with the search algorithm based on stochastic schemata exploiter (SSE) in GE and GEMC. SSE is designed to find the optimal solution of the function, which is the same as GA. The convergence speed of SSE is much higher than that of GA. Moreover, the selection and crossover operators are not necessary for SSE. When GA is replaced with SSE, the improved algorithms of GE and GEM Care named “grammatical evolution by using stochastic schemata exploiter (GE-SSE)” and “grammatical evolution with multiple chromosome by using stochastic schemata exploiter (GEMC-SSE)”, respectively. In this study, GE-SSE is compared with GE in the symbolic regression problem of polynomial function. The results show that the convergence speed of GE-SSE is higher than that of original GE. Next, GE-SSE and GEMC-SSE are compared in stock price prediction problem. The results show that the convergence speed of GEMC-SSE is slightly higher than that of GE-SSE.