Discovery Of Usage Patterns Research Articles

Modelling and Analysis of IPTV Usage Patterns for Improving Quality of Service

Of late IPTV usage is growing rapidly as the viewers are interested to watch stored videos interactively over IP. The bursts in user demands for on-demand content can cause unexpected burden on the content dissemination infrastructure. Stated differently the usage dynamics of video content has its impact on the responsiveness, bandwidth and server. Especially it is non-trivial to solve the problem when number of subscribers is very huge. Here comes the need for modelling and the usage patterns of IPTV and analysing it for making important strategies for server to cope with bursts of subscriber requests. The discovery of usage patterns also considers periods of usage such as week day and week end. In this paper a framework is proposed that can help in modelling and analysis of IPTV usage patterns. The video streaming control events are also considered for the modelling. Characterization of stream control events using a finite state machine with and estimated Markov chain is made. The proposed modelling is validated with traces of operational IPTV environment in large scale.

A cloud-based system for dynamically capturing appliance usage relations

Nowadays, owing to the great advent of sensor technology, data can be collected easily. Mining Internet of Things IoT data has attracted researchers' attention owing to its practicability. Mining smart home data is one significant application in the IoT domain. Generally, the usage data of appliances in a smart environment are generated progressively; visualising how appliances are used from huge amount of data is a challenging issue. Hence, an algorithm is needed to dynamically discover appliance usage patterns. Prior studies on usage pattern discovery are mainly focused on discovering patterns while ignoring the dynamic maintenance of mined results. In this paper, a cloud-based system, Dynamic Correlation Mining System DCMS, is developed to incrementally capture the usage correlations among appliances in a smart home environment. Furthermore, several pruning strategies are proposed to effectively reduce the search space. Experimental results indicate that the developed system is efficient in execution time and possesses great scalability. Subsequent application of DCMS on a real data set also demonstrates the practicability of mining smart home data.

Significant Correlation Pattern Mining in Smart Homes

Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.

Handheld Usage Data Mining for Handheld Data Protection

Mobile handheld devices such as smart cellular phones are easily lost or stolen because of their small sizes and high mobility. Personal data such as addresses and messages stored in the devices may be revealed when the devices are lost. devices must include rigorous and convenient handheld data protection in case the devices are lost or stolen. This research proposes a novel approach for handheld data protection by using handheld usage data mining, which consists of five steps: (i) usage data gathering, (ii) usage data preparation, (iii) usage pattern discovery, (iv) usage pattern analysis and visualization, and (v) usage pattern applications. usage data is collected before applying this method. Usage patterns are discovered and saved by using finite automaton, which is then used to check device usage. When an unusual usage pattern such as an unlawful user trying to access the handheld data is detected, the device will automatically lock itself down until an action, such as entering a password, is taken. Experimental results show this method is effective and convenient for handheld data protection. Keywords: Security, Mobile Devices, Smartphones, Data Mining, Usage Mining, Usage Pattern Discovery, and Identification To cite this document: Wen-Chen Hu, Naima Kaabouch, Lei Chen, and Hung-Jen Yang, Handheld Usage Data Mining for Data Protection, Contemporary Management Research, Vol. 9, No. 2, pp.93-108, 2013. Permanent link to this document http://dx.doi.org/10.7903/cmr.3845

Sequential Pattern Discovery from Web Log Data

mining from the web log data leads to discovery of usage patterns of the user who navigate the web. Patterns which appear frequently in the web log data are item-sets and sequences. In this paper, a novel algorithm Intelligent Generalized Sequential pattern (IGSP) is designed which shows better results than the Generalized Sequential Pattern (GSP) algorithm. Experiment is conducted with respect to running time and number of patterns discovered from the log data and results has shown that IGSP outperforms the well- known algorithms (GSP) algorithm.

Characterizing ADSL customer behaviours by network traffic data-mining

The very rapid adoption of new applications by some segments of the adsl customers may have a strong impact on the quality of service delivered to all customers. This makes the segmentation of adsl customers according to their network usage a critical step both for a better understanding of the market and for the prediction and dimensioning of the network. Relying on a “bandwidth only” perspective to characterize network customer behaviour does not allow the discovery of usage patterns in terms of applications. In this paper, we shall describe how data mining techniques applied to network measurement data can help to extract qualitative and quantitative knowledge.