Informatics Publishing Limited

D. Muruga Radha Devi ¹, P. Thambidurai ²

Affiliations
1 Sathyabama University, Chennai–119, IN
2 Department of CSE, PKIET, Karaikal, IN

Abstract

Time series data are commonly used in data mining. Clustering is the most frequently used method for exploratory data analysis. In this paper a model is proposed for similarity search in recent biased time series databases based on different clustering methods. In recent biased analysis, data are much more interesting and useful for predicting future data than old ones. So in our method, we try to reduce data dimensionality by keeping more detail on recent data than older data. Due to "Dimensionality Curse" the original data is mapped into a feature space by means of Vari-segmented Discrete Wavelet Transform1 and then similarity measurement is performed by applying different clustering methods like Self Organizing Map (SOM), Hierarchical and K-means Clustering. This model is tested using Control Chart Data and the clustering result observed proves that the proposed model is better in grouping similar series under various resolutions.

Keywords

Clustering, Dimensionality Reduction, Discrete Wavelet Transform, Feature Extraction, Hierarchical Clustering, K-means Clustering, Self Organizing Map, Similarity Measurement

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R. Kalpana ¹, P. Thambidurai ², H. Shahul Hamead ¹

Affiliations
1 Department of Computer Science & Engineering, Pondicherry Engineering College, Pondicherry-605 014, IN
2 Perunthalaivar Kamarajar Institute of Engineering and Technology (PKIET), Karaikal–609 603, KZ

Abstract

Computing shortest path from one node to another node in a directed graph is a more general job. This problem was originally sorted by Dijkstra’s algorithm. Many heuristic algorithms were proposed to speedup the standard Dijkstra’s algorithm. The focus of this work is combination of two speed-up techniques called Arc Flag Method and Landmark Approach for Dijkstra’s algorithm. In the arc-flag approach, the entire network is preprocessed to generate additional information is allowed and stored, which is then used to speedup shortest-path queries. In Landmark approach, the identified landmarks are fixed at different intervals, which possess the distance between landmark node and other nodes present in the graph. This paper combines the above two approaches and stores landmark information on arc flags which in turn would generate greater speedup factor. The main objective of this paper is to increase the speedup factor of shortest path algorithm by performing combination of two reach based heuristic techniques such as arc flag and landmark that suits good for practical approach and to reduce down the space consumed by the algorithm by introducing start pointers.

Keywords

Arc Flag, Land Mark, Speedup, Graph, Shortest Path.

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D. Muruga Radha Devi ¹, P. Thambidurai ²

Affiliations
1 Sathyabama University, Chennai, Tamil Nadu, IN
2 Computer Science & Engineering Department, Perunthalaivar Kamarajar College of Institute and Technology, Karaikal, Pondicherry, IN

Abstract

Similarity search in time series databases is an important research direction. Research in this field has focused on the development of effective transformation techniques, the application of dimensionality reduction methods and the design of efficient indexing schemes. In the case where time series are continuously updated with new values (streaming time series), the similarity problem becomes even more difficult to solve, since we must take into consideration the new values of the series. The challenge in a streaming database is to provide efficient algorithms and access methods for query processing, taking into consideration the fact that the database changes continuously as new data become available. Traditional access methods that continuously update the data are considered inappropriate, due to significant update costs. To attack the problem, significant research has been performed towards the development of effective and efficient methods for streaming time series processing. In this paper, we introduce the most important issues concerning similarity search in recent biased time series databases, presenting an efficient technique for similarity query processing in streaming time series using sliding windows. The proposed method called as adaptive stream processing is based on an incremental computation of Discrete wavelet transform which is used as a feature extraction method. In order to prove the efficiency of the proposed method, experiments have been performed for range query and k-nearest neighbor query on real-life data sets. The results have shown that the adaptive stream processing method exhibit consistently better performance in comparison to previously proposed approaches.

Keywords

Similarity Search, Recent Biased Time Series, Sliding Window, Feature Extraction, Stream Processing.

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A. Kumar ¹, P. Thambidurai ²

Affiliations
1 Department of Computer Science & Engineering, Sathyabama University, Chennai, IN
2 Department of Computer Science & Engineering, Pondicherry Engineering College, Puducherry, IN

Abstract

Web-based product and recommendation systems have become ever popular on-line business practice with increasing emphasis on modeling customer needs and providing them with targeted or personalized service solutions in real-time interaction. Recommender systems is a specific type of information filtering system technique that attempts to recommend information items like images, web pages, etc that are likely to be of interest to the user. Normally, a recommender system compares a user profile to some reference characteristics, and seeks to predict the 'rating' and retrieve the query elements. This system can be classified into two groups: one is Content-based recommendation and another is collaborative recommendation system. Content based recommendation tries to recommend web sites similar to those web sites the user has liked, whereas collaborative recommendation tries to find some users who share similar tastes with the given user and recommends web sites they like to that user. Based on web usage data in adoptive association rule based web mining the association rules were applied to personalization. The technique makes use of apriori algorithm to generate association rules. Even this method has some disadvantages. An effective Fuzzy Association Rule Mining Algorithm (FARM) is proposed by the author to overcome those disadvantages. This proposed Fuzzy HARM algorithm for association rule mining in web recommendation system results in better quality and performance.

Keywords

Fuzzy Healthy Association Rule Mining, Association Rules, Apriori Algorithm, Collaborative Recommender.

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Santhi Baskaran ¹, P. Thambidurai ²

Affiliations
1 Department of Information Technology, Pondicherry Engineering College, Puducherry, IN
2 Perunthalaivar Kamarajar Institute of Engineering and Technology (PKIET), Karaikal, IN

Abstract

Energy consumption is a critical design issue in real-time systems, especially in battery-operated systems. The focus is to provide tools to check if a real-time application meets its temporal constraint while minimizing system energy consumption. With this simulator tool, an application is defined by a set of processors, tasks,shared resources and messages. This provides feasibility tests in the case of uniprocessor, multiprocessor and distributed systems. It also provides a flexible simulation engine which allows the designer to describe and run simulations of specific systems.

Keywords

Dynamic Voltage Scaling, Real-Time Scheduling, Simulation Tool, Voltage Scalable Processor.

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R. Kalpana ¹, P. Thambidurai ²

Affiliations
1 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, IN
2 Perunthalaivar Kamarajar Institute of Engineering and Technology, Karaikal, Puducherry, IN

Abstract

The Dijkstra's algorithm is applied in many real world problems like mobile routing, road maps, railway networks, etc,. There are many techniques available to speedup the algorithm while guaranteeing the optimality of the solution. Almost all of the speedup techniques have a substantial amount of parallelism that can be exploited to decrease its running time. By suitably modifying portions of the existing system various degrees of parallelism can be achieved. The rapidly growing field of multiprocessing systems and multi-core processors provide many opportunities for such improvements. In these techniques there's always a demand for the running time and the time required for pre-processing. Space requirements for the pre-processing also have a major influence on the running time of the algorithm.

The main focus of the work is to implement landmark technique and to identify the segment of the code in landmark pre-processing which can be parallelized to obtain better speedup. The results are applied to the combined speedup technique which is based on landmarks and containers. The experimental results were compared and analysed for determining better performance improvements in random graphs and planar graphs.

Keywords

Dijkstra's Algorithm, Graph Theory, Parallel Execution, Speed-Up.

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Santhi Baskaran ¹, P. Thambidurai ²

Affiliations
1 Department of Information Technology, Pondicherry Engineering College, Puducherry, IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, IN

Abstract

Energy consumption is a critical design issue in real-time systems, especially in battery- operated systems. Maintaining high performance, while extending the battery life between charges is an interesting challenge for system designers. Dynamic Voltage Scaling (DVS) allows a processor to dynamically change speed and voltage at run time, thereby saving energy by spreading run cycles into idle time. Knowing when to use full power and when not, requires the cooperation of the operating system scheduler. Usually, higher processor voltage and frequency leads to higher system throughput while energy reduction can be obtained using lower voltage and frequency. Instead of lowering processor voltage and frequency as much as possible, energy efficient real-time scheduling adjusts voltage and frequency according to some optimization criteria, such as low energy consumption or high throughput, while it meets the timing constraints of the real-time tasks. As the quantity and functional complexity of battery powered portable devices continues to raise, energy efficient design of such devices has become increasingly important. Many real-time scheduling algorithms have been developed recently to reduce energy consumption in the portable devices that use DVS capable processors. Extensive power aware scheduling techniques have been published for energy reduction, but most of them have been focused solely on reducing the processor energy consumption. While the processor is one of the major power hungry units in the system, other peripherals such as network interface card, memory banks, disks also consume significant amount of power. Dynamic Power Down (DPD) technique is used to reduce energy consumption by shutting down the processing unit and peripheral devices, when the system is idle. Three algorithms namely Red Tasks Only (RTO), Blue When Possible (BWP) and Red as Late as Possible (RLP) are proposed in the literature to schedule the real-time tasks in Weakly-hard real-time systems. This paper proposes optimal slack management algorithms to make the above existing weakly hard real-time scheduling algorithms energy efficient using DVS and DPD techniques.

Keywords

Weakly-Hard Real-Time System, Skippable Periodic Task, Energy Efficient Scheduling, DVS, DPD.

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