Using static suffix array in dynamic application: Case of text compression by longest first substitution

Over the last decade, enhanced suffix arrays (ESA) have replaced suffix trees in many applications. Algorithms based on ESAs require less space, while allowing the same time efficiency as those based on suffix trees. However, this is only true when a suffix structure is used as a static index. Suffix trees can be updated faster than suffix arrays, which is a clear advantage in applications that require dynamic indexing. We show that for some dynamic applications a suffix array and the derived LCP-interval tree can be used in such a way that the actual index updates are not necessary. We demonstrate this in the case of grammar text compression with longest first substitution and provide the source code. The proposed algorithm has O(N²)$O(N^2)$ worst case time complexity but runs in O(N)$O(N)$ time in practice.     

Prediction of the natural gas consumption in chemical processing facilities with genetic programming

Cinkarna Ltd. is a chemical processing company in Slovenia and the country’s largest manufacturer of titanium oxides (TiO₂). Chemical processing and titanium oxide manufacturing in particular requires high natural gas consumption, and it is difficult to accurately pre-order gas from suppliers. In accordance with the Energy Agency of the Republic of Slovenia regulations, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences total 1.11 % of supplied natural gas costs (average 50,960 EUR per year). This paper presents natural gas consumption prediction and the minimization of associated costs. The data on daily temperature, steam boilers, sulfur acid and TiO₂ production was collected from January 2012 until November 2014. Based on the collected data, a linear regression and a genetic programming model were developed. Compared to the specialist’s prediction of natural gas consumption, the linear regression and genetic programming models reduce the charges for the differences between the ordered and the actually supplied quantities by 3.00 and 5.30 times, respectively. Also, from January until November 2014 the same genetic programming model was used in practice. The results show that in a similar gas consumption regime the differences between the ordered and the actually supplied quantities are statistically significant, namely, they are 3.19 times lower (t test, p < 0.05) than in the period in which the specialist responsible for natural gas consumption made the predictions.     

Business Intelligence Acceptance: The Prominence of Organizational Factors

The specifics of business intelligence systems compared to operational IS motivate the necessity to research the business intelligence systems acceptance determinants separately. The authors followed an exploratory approach in order to conceptualize a business intelligence acceptance model. Their findings show that in the Business Intelligence Systems context, there is a significant emphasis on organizational factors, such as result demonstrability, social influence, and facilitating conditions with sufficient resources that help build an adequate information culture all substantially influencing the effective acceptance of business intelligence systems.     

Formulating the template ILP consistency problem as a constraint satisfaction problem

Inductive Logic Programming (ILP) deals with the problem of finding a hypothesis covering positive examples and excluding negative examples, where both hypotheses and examples are expressed in first-order logic. In this paper we employ constraint satisfaction techniques to model and solve a problem known as template ILP consistency, which assumes that the structure of a hypothesis is known and the task is to find unification of the contained variables. In particular, we present a constraint model with index variables accompanied by a Boolean model to strengthen inference and hence improve efficiency. The efficiency of models is demonstrated experimentally.     

Batch sequencing and cooperation

Game theoretic analysis of sequencing situations has been restricted to manufacturing systems which consist of machines that can process only one job at a time. However, in many manufacturing systems, operations are carried out by batch machines which can simultaneously process multiple jobs. This paper aims to extend the game theoretical approach to the cost allocation problems arising from sequencing situations on systems that consist of batch machines. To analyze the allocation problem at hand, it focusses on the existence of core elements, convexity, and the Shapley value.     

Small‐signal and noise modeling of class of HEMTs using knowledge‐based artificial neural networks

In this article, the neural network approach is exploited for development of bias‐dependent small‐signal and noise models of a class of microwave field effect transistor (FETs) made in the same technology but differing in the gate width. The prior knowledge neural approach is applied. Introducing gate width at the input of proposed neural networks, as well as the S/noise parameters of a device that belongs to the same class as the modeled device representing the prior knowledge, leads to very accurate scattering and noise parameters' modeling, as exemplified by modeling of class of pseudomorphic high electron mobility transistor (pHEMT) devices. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Limitations of a Mapping Algorithm with Fragmentation Mimics (MAFM) when modeling statistical data sources based on measured packet network traffic

Knowing the estimation of a statistical process’s parameters for measured network traffic is very important as it can then be further used for the statistical analyses and modeling of network traffic in simulation tools. It is for this reason that different estimation methods are proposed that allow estimations of the statistical processes of network traffic. One of them is our own histograms comparison (EMHC) based method that can be used to estimate statistical data-length process parameters from measured packet traffic. The main part of EMHC method is Mapping Algorithm with Fragmentation Mimics (MAFM).