High Performance CDR Processing with MapReduce

A call detail record (CDR) is a data record produced by telecommunication equipment consisting of call detail transaction logs. It contains valuable information for many purposes in several domains, such as billing, fraud detection and analytical purposes. However, in the real world these needs face a big data challenge. Billions of CDRs are generated every day and the processing systems are expected to deliver results in a timely manner. The capacity of our current production system is not enough to meet these needs. Therefore a better performing system based on MapReduce and running on Hadoop cluster was designed and implemented. This paper presents an analysis of the previous system and the design and implementation of the new system, called MS2. In this paper also empirical evidence is provided to demonstrate the efficiency and linearity of MS2. Tests have shown that MS2 reduces overhead by 44% and speeds up performance nearly twice compared to the previous system. From benchmarking with several related technologies in large-scale data processing, MS2 was also shown to perform better in the case of CDR batch processing.  When it runs on a cluster consisting of eight CPU cores and two conventional disks, MS2 is able to process 67,000 CDRs/second

Component replication in application servers

Three-tier middleware architecture is commonly used for hosting large-scale distributed applications. Typically the application is decomposed into three layers: front-end, middle tier and back-end. Front-end ("Web server") is responsible for handling user interactions and acts as a client of the middle tier, while back-end provides storage facilities for applications. Middle tier (' Application server') is usually the place where all computations are performed, so this layer provides middleware services for transactions, security and so forth. The benefit of this architecture is that it allows flexible configuration such as partitioning and clustering for improved performance and scalability. On this architecture, availability measures, such as replication, can be introduced in each tier in an application specific manner. Among the three tier described above, the availability of the middle tier and the back-end tier are the most important, as these tiers provide the computation and the data for the applications. This thesis investigates how replication for availability can be incorporated within the middle and back-end tiers. The replication mechanisms must guarantee exactly once execution of user request despite failures of application and database servers. The thesis develops an approach that requires enhancements to the middle tier only for supporting replication of both the tiers. The design, implementation and performance evaluation of such a middle tier based replication scheme for multi-database transactions on a widely deployed open source application server (1Boss) are presented.EThOS - Electronic Theses Online ServiceQUE Project, Department of Informatics, ITB, Bandung, IndonesiaGBUnited Kingdo

Multi-user spectrum analyzer using Java-based client-server application

Spectrum analyzer is used to support laboratory activity for students, and also used by company providing telecommunication service and government agencies to monitor frequency utilization. Today, the price of spectrum analyzer is relatively expensive, leading universities and companies in need of such tools are generally able to provide in a limited amount. Based on these problems, the authors propose a client-server application that allows multiple users to simultaneously visualize measurements made by spectrum analyzer in real time with different parameters. This application is implemented in Java programming language that can be used in a wide range of platforms. After implementation and testing, this application can be used by more than one client at the same time and by using different parameters. Thus, this application can minimize the amount of spectrum analyzer required. In addition, this application also provides several other advantages, such as remote use, minimize maintenance costs and extend the lifetime of the gauge

Compact-Fusion Feature Framework for Ethnicity Classification

In computer vision, ethnicity classification tasks utilize images containing human faces to extract ethnicity labels. Ethnicity is one of the soft biometric feature categories useful in data analysis for commercial, public, and health sectors. Ethnicity classification begins with face detection as a preprocessing process to determine a human’s presence; then, the feature representation is extracted from the isolated facial image to predict the ethnicity class. This study utilized four handcrafted features (multi-local binary pattern (MLBP), histogram of gradient (HOG), color histogram, and speeded-up-robust-features-based (SURF-based)) as the basis for the generation of a compact-fusion feature. The compact-fusion framework involves optimal feature selection, compact feature extraction, and compact-fusion feature representation. The final feature representation was trained and tested with the SVM One Versus All classifier for ethnicity classification. When it was evaluated in two large datasets, UTKFace and Fair Face, the proposed framework achieved accuracy levels of 89.14%, 82.19%, and 73.87%, respectively, for the UTKFace dataset with four or five classes and the Fair Face dataset with four classes. Furthermore, the compact-fusion feature with a small number of features at 4790, constructed based on conventional handcrafted features, achieved competitive results compared with state-of-the-art methods using a deep-learning-based approach