Handling Evolution in Big Data Architectures

Scalable Big Data Systems: Architectures and Optimizations

A Study of Data Management Technology for Handling Big Data

The amount of data is increasing daily. Data requires storage and effective processing for information retrieval. These both are challenge in case of the BigData due its velocity, variety and volume. It requires different management and efficient information retrieval schemes. There are different techniques available for the management of the Bigdata. The distribution of the storage and the pro...

MDM: Governing Evolution in Big Data Ecosystems

On-demand integration of multiple data sources is a critical requirement in many Big Data settings. This has been coined as the data variety challenge, which refers to the complexity of dealing with an heterogeneous set of data sources to enable their integrated analysis. In Big Data settings, data sources are commonly represented by external REST APIs, which provide data in their original form...

Pattern-guided Big Data Processing on Hybrid Parallel Architectures

The advent of hybrid CPU-GPU architectures has significantly increased the number of raw FLOP/s. However, it is not obvious how these can be put to use when processing Big Data. In this paper, we present an approach for designing Big Data simulations for hybrid architectures, which is based on a hierarchal application of design patterns in parallel programming. We provide a detailed account of ...

New Database Architectures: Steps towards Big Data Processing

Both research and practice indicate that traditional universal DBMS architecture hardly satisfies new trends in data processing, particularly in the context of cloud computing and Big Data problems. New database architectures and their basic features will be described, particularly their horizontal scalability and concurrency model, which is mostly weaker than ACID transactions in relational SQ...