Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Performance Modeling and Analysis of Software Architectures Specified Through Graph Transformations
Get PDFSoftware architecture plays an important role in the success of modern, large and distributed software systems. For many of the software systems -- especially safety-critical ones -- it is important to specify their architectures using formal modeling notations. In this case, it is possible to assess different functional and non-functional properties on the designed models. Graph Transformation System (GTS) is a formal yet understandable language which is suitable for architectural modeling. Most of the existing works done on architectural modeling and analysis by GTS are concentrated on functional aspects, while for many systems it is crucial to consider non-functional aspects for modeling and analysis at the architectural level. In this paper, we present an approach to performance analysis of software architectures specified through GTS. To do so, we first enrich the existing architectural style -- specified through GTS - with performance information. Then, the performance models are generated in PEPA (Performance Evaluation Process Algebra) -- a formal language based on the stochastic process algebra -- using the enriched GTS models. Finally, we analyze different features like throughput, utilization of different software components, etc. on the generated performance models. All the main concepts are illustrated through a case study
Probabilistic Skyline Queries over Uncertain Moving Objects
Get PDFData uncertainty inherently exists in a large number of applications due to factors such as limitations of measuring equipments, update delay, and network bandwidth. Recently, modeling and querying uncertain data have attracted considerable attention from the database community. However, how to perform advanced analysis on uncertain data remains an interesting question. In this paper, we focus on the execution of skyline computation over uncertain moving objects. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline at a certain time point, therefore a p-t-skyline contains those moving objects whose skyline probabilities are at least p at time point t. Computing probabilistic skyline over a large number of uncertain moving objects is a daunting task in practice. In order to efficiently compute the probabilistic skyline query, we propose a discrete-and-conquer strategy, which follows the sampling-bounding-pruning-refining procedure. To further reduce the skyline computation cost, we propose an enhanced framework that is based on a multi-dimensional indexing structure combined with the discrete-and-conquer strategy. Through extensive experiments with synthetic datasets, we show that the framework can efficiently support skyline queries over uncertain moving object and is scalable on large data sets
Efficiently Using Prime-Encoding for Mining Frequent Itemsets in Sparse Data
Get PDFIn the data mining field, data representation turns out to be one of the major factors affecting mining algorithm scalability. Mining Frequent Itemsets (MFI) is a data mining problem that is heavily affected by this fact. The vertical approach is one of the successful data representations adopted for MFI problem. The main advantage of this approach is support for fast frequency counting via joining operations. Recently, an encoding method called prime-encoding is proposed as an enhancement for the vertical approach [10]. The performance study introduced in [10] confirmed the high quality of prime-encoding based vertical mining of frequent sequence over other vertical and horizontal ones in terms of space and time. Though sequence mining is more general than itemset mining, this paper presents a prime-encoding based vertical mining of frequent itemsets with new optimizations and a new re-encoding method that further enhance memory and speed. The experimental results show that prime encoding based vertical itemset mining is suitable for high-dimensional sparse data
AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service
Get PDFRecent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described
Discovering Strategic Behaviour of Multi-Agent Systems in Adversary Settings
Get PDFCan specific behaviour strategies be induced from low-level observations of two adversary groups of agents with limited domain knowledge? This paper presents a domain-independent Multi-Agent Strategy Discovering Algorithm (MASDA), which discovers strategic behaviour patterns of a group of agents under the described conditions. The algorithm represents the observed multi-agent activity as a graph, where graph connections correspond to performed actions and graph nodes correspond to environment states at action starts. Based on such data representation, the algorithm applies hierarchical clustering and rule induction to extract and describe strategic behaviour. The discovered strategic behaviour is represented visually as graph paths and symbolically as rules. MASDA was evaluated on RoboCup. Both soccer experts and quantitative evaluation confirmed the relevance of the discovered behaviour patterns
Parallel Processing of Massive Remote Sensing Images in a GPU Architecture
Get PDFProfiting from the development of space remote sensing technology, the amount of remote sensing image data obtained by satellite is increasing dramatically; however, how to deal with these data quickly and efficiently has turned out to be a great computational challenge. With the rapid development of general-purpose GPU computing technology, researchers improved remote sensing applications based on GPU, and obtained good speedup. However, the current GPU parallel processes are not well adapted to the remote sensing image processing; furthermore, they have data loading, storage, and I/O problems. To solve these bottlenecks, this paper proposes three corresponding optimization strategies, and their effectiveness is confirmed by further experiments
The Enhancement of a Computer System for Sorting Capabilities Using FPGA Custom Architecture
Get PDFThe primary goal of the presented experiment was to judge the usefulness of FPGA technology in the sorting operation performed by computer systems. We were interested to see if it was possible to achieve better system performance and lower energy consumption when the CPU is supported by FPGA chips. The method of custom processing was applied. We proposed dedicated sorting hardware to increase performance and save energy. Our concept addresses High Throughput Computing (HTC) systems. The custom hardware approach was proposed because this technique is available in supercomputing infrastructures today. Another important issue of the work is that the hardware was programmed using High Level Language (HLL). As a semiconductor platform for hardware implementation the FPGA was chosen. We evaluated the efficiency of an FPGA based sorting processor that was programmed in Mitrion-C HLL. The FPGA approach was compared to the CPU approach in terms of efficiency and power consumption
An Efficient Visual Analysis Method for Cluster Tendency Evaluation, Data Partitioning and Internal Cluster Validation
Get PDFVisual methods have been extensively studied and performed in cluster data analysis. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods such as Enhanced-Visual Assessment Tendency (E-VAT) algorithm generally represent D as an n times n image I( overlineD) where the objects are reordered to expose the hidden cluster structure as dark blocks along the diagonal of the image. A major constraint of such methods is their lack of ability to highlight cluster structure when D contains composite shaped datasets. This paper addresses this limitation by proposing an enhanced visual analysis method for cluster tendency assessment, where D is mapped to D' by graph based analysis and then reordered to overlineD' using E-VAT resulting graph based Enhanced Visual Assessment Tendency (GE-VAT). An Enhanced Dark Block Extraction (E-DBE) for automatic determination of the number of clusters in I( overlineD') is then proposed as well as a visual data partitioning method for cluster formation from I( overlineD') based on the disparity between diagonal and off-diagonal blocks using permuted indices of GE-VAT. Cluster validation measures are also performed to evaluate the cluster formation. Extensive experimental results on several complex synthetic, UCI and large real-world data sets are analyzed to validate our algorithm
Facial Image Reconstruction from a Corrupted Image by Support Vector Data Description
Get PDFThis paper proposes a method of automatic facial reconstruction from a facial image partially corrupted by noise or occlusion. There are two key features of this method; the one is the automatic extraction of the correspondences between the corrupted input face and reference face without additional manual tasks; the other is the reconstruction of the complete facial information from corrupted facial information based on these correspondences. In this paper, we propose a non-iterative approach that can match multiple feature points in order to obtain the correspondences between the input image and the reference face. Furthermore, shape and texture of the whole face are reconstructed by SVDD (Support Vector Data Description) from the partial correspondences obtained by matching. The experimental results of facial image reconstructions show that the proposed SVDD-based reconstruction method gives smaller reconstruction errors for a facial image corrupted by Gaussian noise and occlusion than the existing linear projection reconstruction method with a regulation factor. The proposed method also reduces the mean intensity error per pixel by an average of 35 %, especially in the reconstruction of a facial image corrupted by Gaussian noise
Group Recommendations: Survey and Perspectives
Get PDFThe popularity of group recommender systems has increased in the last years. More and more social activity is generated by users over the Web and thus not only domains as TV, music or holidays are used and researched anymore for group recommendation, but also collaborative learning support, digital libraries and other domains seems to be promising for group recommendation. Moreover, principles of group recommenders can be used in order to overcome some single user recommendation shortcomings, such as cold start problem. Numerous group recommenders have been proposed, they differ in application domains which are specific in group characteristics. Today's group recommenders do not include and use the power of social aspects (group structure, social status etc.), which can be extracted and derived from the group. We provide a survey of group recommendation principles for the Web domain and discuss trends and perspectives in this field