Improving Data Quality by Leveraging Statistical Relational Learning. Visengeriyeva, Larysa; Akbik, Alan; Kaul, Manohar; Rabl, Tilmann; Markl, Volker in Proceedings of the 21st International Conference on Information Quality, Ciudad Real, Spain, 22-23 June, 2016 (2016).
Application-Level Benchmarking of Big Data Systems. Baru, Chaitanya; Rabl, Tilmann in Big Data Analytics: Methods and Applications (2016). 189–199.
The increasing possibilities to collect vast amounts of data—whether in science, commerce, social networking, or government—have led to the “big data” phenomenon. The amount, rate, and variety of data that are assembled—for almost any application domain—is necessitating a re-examination of old technologies and development of new technologies to get value from the data, in a timely fashion. With increasing adoption and penetration of mobile technologies, and increasing ubiquitous use of sensors and small devices in the so-called Internet of Things, the big data phenomenon will only create more pressures on data collection and processing for transforming data into knowledge for discovery and action. A vibrant industry has been created around the big data phenomena, leading also to an energetic research agenda in this area. With the proliferation of big data hardware and software solutions in industry and research, there is a pressing need for benchmarks that can provide objective evaluations of alternative technologies and solution approaches to a given big data problem. This chapter gives an introduction to big data benchmarking and presents different proposals and standardization efforts.
Towards Streamlined Big Data Analytics. Benczúr, András A.; Pálovics, Róbert; Balassi, Márton; Markl, Volker; Rabl, Tilmann; Soto, Juan; Hovstadius, Björn; Dowling, Jim; Haridi, Seif in ERCIM News (2016). 2016(107)
Big Data Benchmarking - 6th International Workshop, WBDB 2015, Toronto, ON, Canada, June 16-17, 2015 and 7th International Workshop, WBDB 2015, New Delhi, India, December 14-15, 2015, Revised Selected Papers Rabl, Tilmann; Nambiar, Raghunath; Baru, Chaitanya K.; Bhandarkar, Milind A.; Poess, Meikel; Pyne, Saumyadipta in Lecture Notes in Computer Science (2016). (Vol. 10044) Springer.
From BigBench to TPCx-BB: Standardization of a Big Data Benchmark. Cao, Paul; Gowda, Bhaskar; Lakshmi, Seetha; Narasimhadevara, Chinmayi; Nguyen, Patrick; Poelman, John; Poess, Meikel; Rabl, Tilmann (2016). 24–44.
With the increased adoption of Hadoop-based big data systems for the analysis of large volume and variety of data, an effective and common benchmark for big data deployments is needed. There have been a number of proposals from industry and academia to address this challenge. While most either have basic workloads (e.g. word counting), or port existing benchmarks to big data systems (e.g.TPC-H or TPC-DS), some are specifically designed for big data challenges. The most comprehensive proposal among these is the BigBench benchmark, recently standardized by the Transaction Processing Performance Council as TPCx-BB. In this paper, we discuss the progress made since the original BigBench proposal to the standardized TPCx-BB. In addition, we will share the thought process went into creating the specification, challenges in navigating the uncharted territories of a complex benchmark for a fast moving technology domain, and analyze the functionality of the benchmark suite on different Hadoop- and non-Hadoop-based big data engines. We will provide insights on the first official result of TPCx-BB and finally discuss, in brief, other relevant and fast growing big data analytic use cases to be addressed in future big data benchmarks.
Apache Flink in Current Research. Rabl, Tilmann; Traub, Jonas; Katsifodimos, Asterios; Markl, Volker in it - Information Technology (2016). 58(4) 157–165.
Recent trends in data collection and the decreasing prices of storage result in constantly growing amounts of analyzable data. These masses of data cannot easily be processed by traditional database systems as these do not allow for a sufficient degree of scalability. Programs especially designed for parallel data analysis on large-scale distributed systems are required. Developing such programs on clusters of commodity hardware is a complex challenge for even the most experienced system developers. Frameworks such as Apache Hadoop are scalable, but – when compared to SQL – extremely hard to program. The open-source platform Apache Flink is a link between conventional database systems and big data analysis frameworks. Flink is based on a fault tolerant runtime for data stream processing, which manages the distribution of data as well as communications within the cluster. A high diversity of use cases can be supported through various interfaces that allow for the implementation of data analysis processes. In this paper, we present an overview of Apache Flink as well as some current research activities on top of the Apache Flink ecosystem.
