Big data analytics refers to the strategy of analysing large volumes of data, or big data. This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. The aim in analysing all this data is to uncover patterns and connections that might otherwise be invisible, and that might provide valuable insights about the users who created it.
Working with big volume of data collected through many applications in multiple storage locations is both challenging and rewarding. Extracting valuable information from data means to combine qualitative and quantitative analysis techniques. One of the main promises of analytics is data reduction with the primary function to support decision-making.
Many big data optimizations have critical performance requirements (e.g., real-time big data analytics), as indicated by volume, variety, velocity and veracity. To accelerate the big data optimization, users typically rely on detailed performance analysis to identify potential performance bottlenecks. However, due to the large scale and high abstraction of existing big data optimization frameworks (e.g., Apache Hadoop MapReduce), it remains a major challenge to tune the massively distributed systems in a fine granularity. To alleviate the challenges of performance analysis, various performance tools have been proposed to understand the runtime behaviours of big data optimization for performance tuning. BA3 is one of the performance tools that can enable this challenges.
The big data problem can be seen as a massive number of data islands, ranging from personal, shared, social to business data. The data in these islands is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analysing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate how the Linked Data approach can enable the Big Data optimization. In particular, the Linked Data approach has recently facilitated the accessibility, sharing, and enrichment of database.
Applying Data Optimization can provide information to decision makers. As the available measurement data grows, the need for available and reliable interpretation also grows. To this, as decision makers require the timely arrival of information, the need for high performance interpretation of measurement data also grows. Big Data optimization techniques can enable designers and engineers to realize large scale monitoring systems in real life, by allowing these systems to comply to real world constrains in the area of performance, reliability and reliability.