The current issue of Computer Science and Information Systems consists of 9 regular articles and 12 papers in the special section on Advances in Information Technology. We are indebted to all our guest editors, authors and reviewers, without whom this issue would not have been possible.

We are happy to announce the new two-year impact factor of our journal for 2015, which is now 0.623 and higher than in previous years. In addition, ComSIS' five-year impact factor of 0.640 is in steady growth.

We start the regular article section with the paper “Extending Identity Management System with Multimodal Biometric Authentication” by Bojan Jovanović et al., which presents a FreeIPA-based identity management system that employs the MMBio framework to include multimodal biometrics into identity management and multifactor authentication. The described system prototype demonstrates the feasibility of identity management and multimodal biometric system integration.

“Multifaceted Service Identification: Process, Requirement and Data” by Mohammad Javad Amiri et al. takes the business domain into account in the process of semi-automatic service identification, combining three strategies: considering task interconnections within business processes, determining task dependencies based on common supporting requirements, and task relations based on business data. Strategy combination is achieved through construction of task-task matrices, from which services are identified by clustering.

Zhenzhen Xu et al. in “Cross-Domain Item Recommendation Based on User Similarity” propose a cross-domain item recommendation model denoted CRUS, which firstly introduces the trust relation among friends into cross-domain recommendation. Considering the observation that friends can exhibit both similarities and differences in various domains, the system modifies the transfer matrix in the random walk, highlighting friends that share similar interests with the target user.

In their article “Intelligent SSD: A Turbo for Big Data Mining”, Duck-Ho Bae et al. address the performance bottleneck regarding the use of solid state drives (SSDs) in data-intensive applications, where the weak link is not the bandwidth of the drive itself (as with hard disk drives), but of the host interface. A new notion of the intelligent SSD is introduced, where a large volume of data can be directly processed by CPU and DRAM inside intelligent SSDs, with the final result of a small size needing to be transferred to the host CPU. The authors show that intelligent SSDs can provide significant improvement in performance over the host CPUs in data-intensive scenarios.

“CESARSC: Framework for creating Cultural Entertainment Systems with Augmented Reality in Smart Cities” by Ángel García-Crespo et al. describes a framework for the creation of cultural entertainment systems and augmented reality, employing cloud-based technologies and real-time interaction of heterogeneous mobile technology in the field of mobile tourism. The proposed system allows players to carry out a series of games and challenges that improve their tourism experience.

M. Robiul Hoque et al. in “Development of Middleware Architecture to Realize Context-Aware Service in Smart Home Environment” propose a middleware methodology for tackling the issues of context acquiring, processing, reasoning, and disseminating to services installed in a smart home. Context is modeled using the Web Ontology Language (OWL), and an improved rule-based reasoning algorithm is presented to infer high-level contexts from available low-level contexts.

The paper “Modeling Dynamical Systems with Data Stream Mining” by Aljaž Osojnik et al. tackles modeling dynamical systems in discrete time using regression trees, model trees and option trees for on-line regression from streaming data, proposing algorithms for constructing each type of trees, and concluding through an extensive set of experiments that option trees perform best out of the considered approaches.

In “Adaptive TDMA Scheduling for Real-Time Flows in Cluster-Based Wireless Sensor Networks,” Gohar Ali et al. propose an adaptive cluster-based scheduling scheme for real-time flows in wireless sensor networks by using time slots for flows according to flow type, thus reducing the number of unused channels for real-time flows compared to the static scheme.

Finally, “Ontology-Based Generated Learning Objects for Mobile Language Learning” by Miloš Milutinović et al. addresses the problem of mobile learning – learning on the move where the learning process is often limited, and content needs to be adapted in order to keep the interactions with the user simple, effective, and motivating. The article presents a model for improving mobile language learning using adaptive techniques, designed to take advantage of opportunities for learning content delivery in authentic learning situations.

Mirjana Ivanović

Managing Editor
Miloš Radovanović