International Journal of Hybrid Information Technology Vol.8, No.3 (2015), pp.155-164 http://dx.doi.org/10.14257/ijhit.2015.8.3.15 ISSN: 1738-9968 IJHIT Copyright ⓒ 2015 SERSC Challenges and Issues in DATA Stream: A Review Muhammad Arif 1,2 , Khubaib Amjad Alam 1,2 and Mehdi Hussain 1,3 1 Faculty of Computer Science and Information Technology, University of Malaya 50603 Kuala Lumpur, Malaysia 2 Computer Science Department, Comsats Institute of Information and Technology Islamabad Pakistan 3 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad Pakistan arifmuhammad36@siswa.um.edu.my, khubaibalam@siswa.um.edu.my mehdi141@siswa.um.edu.my Abstract Data stream is a continuous, time varying, massive and infinitely ordered sequence of data elements. The streaming data are fast changing with time, it is impossible to acquire all the elements in a data stream. Therefore, each data element should be examined at most once in data streams. Memory usage for mining data stream should be limited due to the new data elements are continuously generated from the streams. It is essential to ensure that newly arrived stream should be immediately available whenever it is requested made this task much challenging and necessary for fraud detection in stream, taking out knowledge, for business improvement and other applications where data arrived in stream. This paper tries to highlight important issues and research challenges of data stream by means of a comprehensive review. Keywords: Data Mining, Data Stream, Data Set, Memory, Efficiency, Time Complexity 1. Introduction Data streams, consist of unbounded data in continues fashion and it can be regarded as real time data. Continuous data coming from different areas with a high speed and massive size is called data stream. Computer network traffic, ATM, phone conversations, web searches, transactions and sensor data are the real examples of streaming data. Now question arises that how to tackle these data streams and how to get valuable information. Researchers have developed different techniques in this regard. Some techniques depend on the nature of problem, while some algorithms have been developed for time constraint that one pass algorithm some for memory management that is for limited memory to reduce the usage of memory. But focus has been on specific problems related to data streams. In this paper we have discussed issues related to adverse impact of Radio frequency interference in real time streaming discussed, i.e. How to improve the performance of predictive stream learning algorithms, Load shedding problem of join operator, overloading of Real time system, handling unpredictable network stream, exact processing over data streams, mining online data stream, frequent items in data stream, computational issue, frequency count issue, maximum frequent item sets issue, In-core mining of streaming, online closed frequent items issue, classifier selection, frequency estimation over Sliding Windows, memory consumption, close weighted frequent item sets, distributed privacy, high data speed stream and outlier detection in distributed data streams.