Category Of Information Systems Research Articles

Distance-based double-quantitative rough fuzzy sets with logic operations

Based on various requirements, many generalized rough set models have been developed to alleviate the limitations of generic Pawlak rough set theory and tackle different categories of information systems. One of the limitations is that rough set models based on equivalence relation are only applicable to discrete data information systems, and not suitable for dealing with real-valued continuous data without any prior processing. Another limitation is that “classical” rough sets do not consider the quantitative information about the degree of overlap between equivalence classes and the basic set, so they cannot cope well with the quantification problems. In this paper, we propose a framework of distance-based double-quantitative rough fuzzy set (Db-Dq-RFS) with logic operation by forming a distance-based fuzzy similarity relation in an information system with continuous data to simultaneously solve the two limitations. It is presented how to construct the distance-based fuzzy similarity relation in a normalized information system, and how to use this fuzzy similarity relation to generate distance-based single-quantitative rough fuzzy set (Db-Sq-RFS) models and the Db-Dq-RFS models with logic operation. The proposed Db-Dq-RFS models can overcome certain limitations of the classical rough set model. Following further studies to discuss the decision rules with parameters variation in the four kinds of Db-Dq-RFS models, we present an illustrative example to interpret the proposed developments and to verify the effect of parameters variation on decision rules. To illustrate the effectiveness of the parameters variation on decision rules, experimental evaluation is performed using five datasets coming from the University of California–Irvine (UCI) repository.

56 A hospital management system based on workflow

Objectives Hospital management systems, currently under the network environment, are an emerging category of information systems. The decomposition of hospital management systems into the workflow of mobile agents’ architectures does not seem an appropriate solution, since mobile agents can allow servers to be made more flexible, with components being added and removed at runtime. Mobile agents can reduce network load and permit disconnected operation during which a client can also ‘launch’ a mobile agent into the network. Methods In this paper, we study a hospital management system based on workflow. It can cope with the rapidly evolving business environment better than most other systems as they are more flexible and open. We describe a possible architecture of such a hospital management system. Mobile agents enable a flexible and adaptive system with the possibility of simulation, analysis and monitoring of process execution in order to identify potential inconsistencies and to provide appropriate information to the administrator for the purpose of process improvement. Then we discuss the functionalities and the collaborations of mobile agents, followed by process state transitions and the executing process of the hospital management system. Results We implemented the described architecture and proposed solutions to the problems arising while the hospital management system was being implemented. The prototype design could provide a more flexible and adaptive infrastructure for incorporating dynamic changes into the currently executing process model. Due to a mobile agent system, its modular design both reduces the complexity of the system and is also easy to expand, as well as having good robustness and reliability. Conclusions We have constructed a hospital management system based on workflow which provides more flexibility, adaptability and expansibility, and is more robust and reliable than existing systems. Acknowledgments This work was financially supported in part by the National Natural Science Foundation of China under Grant Nos. 61263023 and 51174103. It was also subsidized by key projects of the Yunnan Education Department (Grant Nos. 2012Z107 and 2013Z129).

Implications of Perceived Utility on Individual Choice and Preferences: A New Framework for Designing Recommender System

ABSTRACT Consumer psychology and consumer behaviour has constantly been a field of interest for the researchers. Volumes of researches are available in this area, still new theories and concepts keep emerging in response to change in context and environment. Consumer psychology has large cross disciplinary implications. In this paper, a relationship between consumer psychology and recommender system has been explained and how implementing a consumer psychology model can improve recommender system performance. This paper also gives first level analysis of various filters that can be developed along with the design of recommender system in order to generate a more refined and relevant choice sets. There are attraction effects observed among different items and the attractiveness of a product is codependent on attractiveness of other options available in a choice set. In the present paper, we have explored the utility of defender model in the design of recommender systems. Different effects like decoy and asymmetric dominance are also analyzed to achieve better efficiency and effectiveness in the design of recommender system. Keywords: Decoy, Recommender, Defender Model, Asymmetric Dominance INTRODUCTION Recommender systems are the category of information systems which supports searching and suggesting products and services according to user's requirement and preferences (Resnick et al., 1997). The product range may vary from consumer durables such as a book in Amazon or digital goods such as song on lastfm. The task of recommending products proactively is performed by recommender systems which work backstage on E-commerce websites. Many recommendation approaches and methods are applied to target internet based consumers. Collaborative filtering was earliest approach to create user to user similarity and then recommend similar products. Recommendation approach was simple. Positively rated items by one set of user were recommended to similar users. However, this 'user to user' correlation lead to scarce data and cold start problems (Ahn, 2008). This problem was targeted by designing an 'item to item' based correlation and this category of algorithms were termed as content based filtering as volume of data available for products was much denser than user data. There were some specialized variations also for example demographic or knowledge based recommender system which includes domain based knowledge to analyze user preference and predict user choices. As it is evident that recommender system directly interact with consumer and for establishing a better communication it is essential that recommender systems should be intelligent enough to understand consumer's behavioral traits(Pu et al., 2011). Understanding consumer psychology and its phenomenon will actually present meaningful insights to improve recommendations and effectiveness of these systems which in turn can be translated into economical benefits. The purpose of the recommender system is not only to elicit preference and produce accurate search results but also to improve the decision making in an ecommerce context (Xiao et al., 2007). A good amount of research in the recommender system focuses on developing complex algorithms and software programs which improves the efficiency of recommender systems (Cosley et al., 2003). However, there is a need to identify other significant aspects which attempts to maximize the value derived from a recommender system experience (Xiao et al., 2007). Factors such as trust on online medium (Yaobin et al., 2007) and personal choices (Ho et al., 2008) are often ignored in the design and implementation process. .In the present paper, we propose a conceptual framework based on established theories from the field of consumer psychology. The proposed framework allows the systems designer to incorporate the significance of derived value of a given product/service in order to achieve the single most important objective of any decision behaviour-maximizing utility. …

Information systems with partially unknown purposes

In “traditional” information systems design theory it is usually assumed that the information system to be designed has, at least in principle, a well-defined purpose, that is, a purpose from which it is possible to derive, among other things, the information needs to be satisfied by the information system. However, there is a category of information systems of growing practical significance, where the purposes are partially unknown. Some examples of types of information systems which typically belong to this category are decision support systems, executive information systems, statistical information systems, scientific information systems, and metainformation systems. When an information system belonging to this category is going to be designed, it is usually impossible to specify its usage with great precision. The business decisions, research problems, or whatever kind of activity it is that the information system is going to support may not yet even have been thought of; they may lie a long time ahead, and by then many things may have changed within the area of interest and its environment, as well as in the focus of interest of the information system users. This paper discusses some of the methodological challenges posed by information systems with partially unknown purposes.

Soft information systems

This paper describes a category of information systems called ‘Soft information systems’. Basically these lie between conventional information systems in which data are well defined, and management support, and decision support systems. A soft system is a system which operates on soft data. Such data cover categories of information such as conjectures, predictions, feelings and opinions and are typically found in systems where there is some element of anticipation of the future. Such systems include career counselling systems, where clients have rather ‘fuzzy’ views of what they might wish to do, through to economic based systems used to predict levels of employment, inflation levels and interest rates etc. In between are management-type systems in which it is normally assumed that hard (i.e. factual) data are used, for instance in budgeting systems. The paper describes some previous work and the experimental systems which tested and explored the ideas involved as well as attempting to refine some of the techniques that can be used to deal with soft data.

The Hoare and Smyth power domain constructors commute under composition

This paper examines the composition of the Smyth and Hoare power domains ( P S and P H ) applied over the class of consistently complete algebraic partial orders represented by Scott's category of information systems. It is proposed that both orders of application yield equivalent domains. This is motivated by interpreting data elements in the Smyth as disjunction of propositions and those in the Hoare as conjunctions (as suggested by Scott), giving the two conjunctive-disjunctive forms for each of the double power domains. Then, using elementary category theory, it is shown that the images of any object under P S ○ P H and under P H ○ P S are isomorphic, and hence that both constructors are equivalent as functors.