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Conference Object Citation - WoS: 6Citation - Scopus: 6Comparing Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms for Solving the Set Covering Problem(Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Galleguillos, Cristian; Barraza, Jorge; Lizama, Sebastian; Munoz, Alexis; Paredes, FernandoThe set covering problem is a classical model in the subject of combinatorial optimization for service allocation, that consists in finding a set of solutions for covering a range of needs at the lowest possible cost. In this paper, we report various approximate methods to solve this problem, such as Cuckoo Search, Bee Colony, Firefly Optimization, and Electromagnetism-Like Algorithms. We illustrate experimental results of these metaheuristics for solving a set of 65 non-unicost set covering problems from the Beasley's OR-Library.Conference Object Some Intrinsic Properties of Interacting Deterministic Finite Automata(Springer-verlag Berlin, 2003) Kiliç, HThe agent controllability of the environment is investigated using simple deterministic interacting automata pair model. For this purpose, a general extended design approach for such couple is developed. In the experiments, we focused on simple binary state case and generated stability/cycle behavior characteristics map for the couple. Examinations on the map showed that the behavior of the couple shows some initial value sensitivity. Also, we observed some non-controllable agent/environment couple definition which may imply an inherent communication border between agent and the environment.Conference Object Citation - WoS: 4Citation - Scopus: 3Systematic Mapping Study on Performance Scalability in Big Data on Cloud Using Vm and Container(Springer-verlag Berlin, 2016) Gokhan, Cansu; Karakaya, Ziya; Yazici, AliIn recent years, big data and cloud computing have gained importance in IT and business. These two technologies are becoming complementing in a way that the former requires large amount of storage and computation power, which are the key enabler technologies of Big Data; the latter, cloud computing, brings the opportunity to scale on-demand computation power and provides massive quantities of storage space. Until recently, the only technique used in computation resource utilization was based on the hypervisor, which is used to create the virtual machine. Nowadays, another technique, which claims better resource utilization, called "container" is becoming popular. This technique is otherwise known as "lightweight virtualization" since it creates completely isolated virtual environments on top of underlying operating systems. The main objective of this study is to clarify the research area concerned with performance issues using VM and container in big data on cloud, and to give a direction for future research.Book Part Multi-disciplinary, Global Student Collaboration(Springer-verlag Berlin, 2014) Milewski, A. E.; Swigger, K.; Serce, F. C.The goal of this study is to understand the dynamics of collaboration within globally-distributed teams working in a realistic Human-System Interaction (further called HSI) environment and Software Engineering context. Quantitative data on communications were collected by capturing virtually all of the communications between the team members. Qualitative data were collected through the interviews conducted by the involved instructors. The results reveal some of the challenges associated with working in interdisciplinary and global settings and suggest areas of caution for such HSI educational experiences in the future.Conference Object Citation - WoS: 2Citation - Scopus: 3Autonomous Tuning for Constraint Programming Via Artificial Bee Colony Optimization(Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Mella, Felipe; Flores, Javier; Galleguillos, Cristian; Misra, Sanjay; Paredes, FernandoConstraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.Conference Object Citation - WoS: 1Citation - Scopus: 1A multi-relational rule discovery system(Springer-verlag Berlin, 2003) Uludag, M; Tolun, MR; Etzold, TThis paper describes a rule discovery system that has been developed as part of an ongoing research project. The system allows discovery of multi-relational rules using data from relational databases. The basic assumption of the system is that objects to be analyzed are stored in a set of tables. Multi-relational rules discovered would either be used in predicting an unknown object attribute value, or they can be used to see the hidden relationship between the objects' attribute values. The rule discovery system, developed, was designed to use data available from any possible 'connected' schema where tables concerned are connected by foreign keys. In order to have a reasonable performance, the 'hypotheses search' algorithm was implemented to allow construction of new hypotheses by refining previously constructed hypotheses, thereby avoiding the work of re-computing.
