Computational Decision Support System for ADHD Identification

Machine Intelligence Research - Attention deficit/hyperactivity disorder (ADHD) is a common disorder among children. ADHD often prevails into adulthood, unless proper treatments are facilitated to...     

Automatic optimization of stream programs via source program operator graph transformations

Distributed data stream processing is a data analysis paradigm where massive amounts of data produced by various sources are analyzed online within real-time constraints. Execution performance of a stream program/query executed on such middleware is largely dependent on the ability of the programmer to fine tune the program to match the topology of the stream processing system. However, manual fine tuning of a stream program is a very difficult, error prone process that demands huge amounts of programmer time and expertise which are expensive to obtain. We describe an automated process for stream program performance optimization that uses semantic preserving automatic code transformation to improve stream processing job performance. We first identify the structure of the input program and represent the program structure in a Directed Acyclic Graph. We transform the graph using the concepts of Tri-OP Transformation and Bi-Op Transformation. The resulting sample program space is pruned using both empirical as well as profiling information to obtain a ranked list of sample programs which have higher performance compared to their parent program. We successfully implemented this methodology on a prototype stream program performance optimization mechanism called Hirundo. The mechanism has been developed for optimizing SPADE programs which run on System S stream processing run-time. Using five real world applications (called VWAP, CDR, Twitter, Apnoea, and Bargain) we show the effectiveness of our approach. Hirundo was able to identify a 31.1 times higher performance version of the CDR application within seven minutes time on a cluster of 4 nodes.     

Energy consumption analysis of data stream processing: a benchmarking approach

Energy efficiency of data analysis systems has become a very important issue in recent times because of the increasing costs of data center operations. Although distributed streaming workloads have increasingly been present in modern data centers, energy‐efficient scheduling of such applications remains as a significant challenge. In this paper, we conduct an energy consumption analysis of data stream processing systems in order to identify their energy consumption patterns. We follow stream system benchmarking approach to solve this issue. Specifically, we implement Linear Road benchmark on six stream processing environments (S4, Storm, ActiveMQ, Esper, Kafka, and Spark Streaming) and characterize these systems' performance on a real‐world data center. We study the energy consumption characteristics of each system with varying number of roads as well as with different types of component layouts. We also use a microbenchmark to capture raw energy consumption characteristics. We observed that S4, Esper, and Spark Streaming environments had highest average energy consumption efficiencies compared with the other systems. Using a neural networkbased technique with the power/performance information gathered from our experiments, we developed a model for the power consumption behavior of a streaming environment. We observed that energy‐efficient execution of streaming application cannot be specifically attributed to the system CPU usage. We observed that communication between compute nodes with moderate tuple sizes and scheduling plans with balanced system overhead produces better power consumption behaviors in the context of data stream processing systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Graph database benchmarking on cloud environments with XGDBench

Online graph database service providers have started migrating their operations to public clouds due to the increasing demand for low-cost, ubiquitous graph data storage and analysis. However, there is little support available for benchmarking graph database systems in cloud environments. We describe XGDBench which is a graph database benchmarking platform for cloud computing systems. XGDBench has been designed with the aim of creating an extensible platform for graph database benchmarking which makes it suitable for benchmarking future HPC systems. We extend the Yahoo! Cloud Serving Benchmark (YCSB) to the area of graph database benchmarking by creation of XGDBench. The benchmarking platform is written in X10 which is a PGAS language intended for programming future HPC systems. We describe the architecture of the XGDBench and explain how it differs from the current state-of-the-art. We conduct performance evaluation of five famous graph data stores AllegroGraph, Fuseki, Neo4j, OrientDB, and Titan using XGDBench on Tsubame 2.0 HPC cloud environment.     

Infoshare : Design and Implementation of Scalable Multimedia Signage Architecture for Wireless Ubiquitous Environments

Digital signage systems have found many interesting applications in the realms of advertising, entertainment and education. One of the most prevalent challenging issues faced by current Local Area Network (LAN) based Digital signage network architectures is that their difficulty in porting to wireless ubiquitous environments. While popularity of wireless LANs promotes such architectural improvement, Traditional thin/thick client based architectures suffer inefficiency and scalability issues introduced by use of proprietary signage content formats. Use of such content formats to store signage contents is less optimal since it could lead to content redundancy, difficulty in creating, managing signage contents and scalability issues. As a solution for this issue we propose a Smart Client based digital signage architecture that uses XAML (an XML based declarative GUI language) contents for expressing its signage displays. While Smart Clients can better tolerate communication disruptions which are quite frequent in wireless environments, use of XAML based open content format promotes use of simple tools and variety of devices for signage content creation and management over the Internet in a ubiquitous environment. We successfully applied this generic architecture to a prototype digital signage system called Infoshare and report its robustness in withstanding network disruptions. We evaluate the easiness of editing XAML based signage contents by comparing Infoshare with a popular LAN based digital signage system which uses proprietary content formats. We demonstrate scalability of Infoshare signage service in terms of hardware resources by deploying it in different hardware platforms.