International Journal of Computer Applications (0975 – 8887) Volume 6– No.1, September 2010 40 A Survey on Mining services for Better Enhancement in Small HandHeld Devices Ganesh Raj Kushwaha M.Tech Scholar, PG Dept. of Computer Science & Engineering RKDF, Bhopal, India Niresh Sharma PG Dept. of Computer Science & Engineering RKDF, Bhopal India ABSTRACT This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in order to discover rules and patterns in a distributed and heterogeneous content environment. For the analysis of structured data, the application covers the whole Knowledge Discovery process. The purpose of the paper is to show how to implement existing techniques in a flexible architecture for providing new added value services. Finally in our paper, a case study of different data mining task is thrive under different category like in WWW, Mobile environment, PDA Devices, Web log techniques etc. We also use MIDP (Mobile Information device Profile) and CLDC (Connected Limited Device Configuration) of J2ME. Keywords J2ME, DMS, CLDC, MIDP 1. INTRODUCTION Pattern Analysis is becoming more popular by the help of the recent developments in the computer and communication technologies. Mobility of the users today gives rise to the problem of mobility management. Modeling the behavior patterns of users in the mobile systems benefits not only the users in smart access, but also the mobile service providers in various fields. In the mobile environments and developments, the users may request various kinds of services and applications by cellular phone, PDA, or notebook from arbitrary locations at any time via GSM, GPRS or wireless networks. Obviously, the behavior pattern and behavior, in which the location and the service are inherently coexistent, of mobile users becomes more complex than that of the traditional web systems. To help the user get desired information in a short time is one of the promising applications, especially in the mobile environments because in today’s scenario everyone wants the desired result in short duration of time Pattern prediction and recognition can be defined as the prediction of a mobile user’s next movement where the mobile user is traveling between the cells of a GSM network. The predicted movement can then be used to increase the efficiency of pattern analysis by using the predicted movement; the system can effectively allocate resources to the most probable-to-move cells instead of blindly allocating excessive resources in the cell neighborhood of a Handheld user. Effective allocation of resources to mobile users would improve resource utilization and reduce the latency in accessing the resources. Accurate prediction of location information is also crucial in processing location-dependent queries of mobile users. When a user submits a location dependent query, the answer to the query will depend on the current location of the user. Many application areas including health care, bioscience, hotel management, and the military benefit from efficient processing of location-dependent queries. With effective prediction of behavior, it may also be possible to answer the queries that refer to the future positions of users and like or dislike in terms of frequent pattern mining. Compared to the amount of work performed on location update, little has been done in the area of mobility prediction. We also analyze about the Mobile Agent which is an independent computer program which can migrate independently in the heterogeneous network, according to certain regulations, and seek for the appropriate computing resource, the information resource or the software resource. It can process the resources in advantage of these resources are in the same host or network and complete the specific task on behalf of the user. In this paper we analyze some of the good thing available in pattern mining and we concentrate on those rules to improve the mining pattern which is helpful in improving mining pattern in small handheld devices like Mobile devices,PDA,notebook etc. There are lot of patterns and rule discovered until now we focus on some of the rules which is much more useful in terms of small devices and suited the memory requirement of these devices. The remaining of this paper is organized as follows. We briefly review the Data Mining in J2ME aspects in Section 2. The empirical evaluation on different data mining services in section 3.Review and conclusion in section 4. The conclusions and future work are given in Section 5. References are given in Section 6. 2. J2ME in Data Mining The core concept which is used in data mining through mobile is based on j2me.J2me consist of mainly two components first is CLDC and second is MIDP. The j2me architecture enables computing data mining thrives by scaled based on constraints on small computing device. J2ME architecture does not replace the architectural configuration and operating system of small computing device but the J2ME architecture consist of layered application located above the host operating system.