H Fadi

Bio: H Fadi is an academic researcher. The author has contributed to research in topics: Unstructured data & IBM. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

Understanding System and Architecture for Big Data

Abstract: The use of Big Data underpins critical activities in all sectors of our society. Achieving the full transformative potential of Big Data in this increasingly digital world requires both new data analysis algorithms and a new class of systems to handle the dramatic data growth, the demand to integrate structured and unstructured data analytics, and the increasing computing needs of massive-scale analytics. In this paper, we discuss several Big Data research activities at IBM Research: (1) Big Data benchmarking and methodology; (2) workload optimized systems for Big Data; (3) case study of Big Data workloads on IBM Power systems. In (3), we show that preliminary infrastructure tuning results in sorting 1TB data in 14 minutes 1 on 10 Power 730 machines running IBM InfoSphere BigInsights. Further improvement is expected, among other factors, on the new IBM PowerLinux TM 7R2 systems.

Guiding the Introduction of Big Data in Organizations: A Methodology with Business- and Data-Driven Ideation and Enterprise Architecture Management-Based Implementation

Abstract: Researchers and practitioners frequently assume that big data can be leveraged to create value for organizations implementing it. Decisions for big data idea generation and implementation need careful consideration of multiple factors. However, no scientifically grounded and unbiased method to structure such an assessment and to guide implementation exists yet. This paper describes a methodology based on IT value theory and workgroup ideation guiding big data idea generation, idea assessment and implementation management. Distinct business and data driven perspectives are distinguished to account for big data specifics. Enterprise Architecture Management and Business Model Generation techniques are used in individual steps for execution. A first prototypical application in the context of Supply Chain Management illustrates the applicability of the method.

Strategic Alignment of Cloud-Based Architectures for Big Data

Abstract: Big Data is an increasingly significant topic for management and IT departments. In the beginning, Big Data applications were large on premise installations. Today, cloud services are used increasingly to implement Big Data applications. This can be done on different ways supporting different strategic enterprise goals. Therefore, we develop a framework that enumerates the alternatives for implementing Big Data applications using cloud-services and identify the strategic goals supported by these Alternatives. The created framework clarifies the options for Big Data initiatives using cloud-computing and thus improves the strategic alignment of Big Data applications.

Memory system characterization of big data workloads

Abstract: Two recent trends that have emerged include (1) Rapid growth in big data technologies with new types of computing models to handle unstructured data, such as map-reduce and noSQL (2) A growing focus on the memory subsystem for performance and power optimizations, particularly with emerging memory technologies offering different characteristics from conventional DRAM (bandwidths, read/write asymmetries). This paper examines how these trends may intersect by characterizing the memory access patterns of various Hadoop and noSQL big data workloads. Using memory DIMM traces collected using special hardware, we analyze the spatial and temporal reference patterns to bring out several insights related to memory and platform usages, such as memory footprints, read-write ratios, bandwidths, latencies, etc. We develop an analysis methodology to understand how conventional optimizations such as caching, prediction, and prefetching may apply to these workloads, and discuss the implications on software and system design.

Towards a Framework for Enterprise Architecture Analytics

Abstract: Current approaches for enterprise architecture lack analytical instruments for cyclic evaluations of business and system architectures in real business enterprise system environments. This impedes the broad use of enterprise architecture methodologies. Furthermore, the permanent evolution of systems desynchronizes quickly model representation and reality. Therefore we are introducing an approach for complementing the existing top-down approach for the creation of enterprise architecture with a bottom approach. Enterprise Architecture Analytics uses the architectural information contained in many infrastructures to provide architectural information. By applying Big Data technologies it is possible to exploit this information and to create architectural information. That means, Enterprise Architectures may be discovered, analyzed and optimized using analytics. The increased availability of architectural data also improves the possibilities to verify the compliance of Enterprise Architectures. Architectural decisions are linked to clustered architecture artifacts and categories according to a holistic EAM Reference Architecture with specific architecture metamodels. A special suited EAM Maturity Framework provides the base for systematic and analytics supported assessments of architecture capabilities.

Towards a big data reference architecture

Abstract: Technologies and promises connected to ‘big data’ got a lot of attention lately. Leveraging emerging ‘big data’ sources extends requirements of traditional data management due to the large volume, velocity, variety and veracity of this data. At the same time, it promises to extract value from previously largely unused sources and to use insights from this data to gain a competitive advantage. To gain this value, organizations need to consider new architectures for their data management systems and new technologies to implement these architectures. In this master’s thesis I identify additional requirements that result from these new characteristics of data, design a reference architecture combining several data management components to tackle these requirements and finally discuss current technologies, which can be used to implement the reference architecture. The design of the reference architecture takes an evolutionary approach, building from traditional enterprise data warehouse architecture and integrating additional components aimed at handling these new requirements. Implementing these components involves technologies like the Apache Hadoop ecosystem and so-called ‘NoSQL’ databases. A verification of the reference architecture finally proves it correct and relevant to practice. The proposed reference architecture and a survey of the current state of art in ‘big data’ technologies guides designers in the creation of systems, which create new value from existing, but also previously under-used data. They provide decision makers with entirely new insights from data to base decisions on. These insights can lead to enhancements in companies’ productivity and competitiveness, support innovation and even create entirely new business models.