A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Rumble: Data Independence for Large Messy Data Sets

This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous and nested collections of JSON objects, leveraging the parallel capabilities of Spark so as to provide a high degree of data independence. The design is based on two key insights: (i) how to map JSONiq expressions to Spark transformations on RDDs and (ii) how to map JSONiq FLWOR clauses to Spark SQL on DataFrames. We have developed a working implementation of these mappings showing that JSONiq can efficiently run on Spark to query billions of objects into, at least, the TB range. The JSONiq code is concise in comparison to Spark's host languages while seamlessly supporting the nested, heterogeneous data sets that Spark SQL does not. The ability to process this kind of input, commonly found, is paramount for data cleaning and curation. The experimental analysis indicates that there is no excessive performance loss, occasionally even a gain, over Spark SQL for structured data, and a performance gain over PySpark. This demonstrates that a language such as JSONiq is a simple and viable approach to large-scale querying of denormalized, heterogeneous, arborescent data sets, in the same way as SQL can be leveraged for structured data sets. The results also illustrate that Codd's concept of data independence makes as much sense for heterogeneous, nested data sets as it does on highly structured tables.Comment: Preprint, 9 page

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

Data transformation as a means towards dynamic data storage and polyglot persistence

Legacy applications have been built around the concept of storing their data in one relational data store. However, with the current differentiation in data store technologies as a consequence of the NoSQL paradigm, new and possibly more performant storage solutions are available to all applications. The concept of dynamic storage makes sure that application data are always stored in the most optimal data store at a given time to increase application performance. Additionally, polyglot persistence aims to push this performance even further by storing each different data type of an application in the data store technology best suited for it. To get legacy applications into dynamic storage and polyglot persistence, schema and data transformations between data store technologies are needed. This usually infers application redesigns as well to support the new data stores. This paper proposes such a transformation approach through a canonical model. It is based on the Lambda architecture to ensure no application downtime is needed during the transformation process, and after the transformation, the application can continue to query in the original query language, thus requiring no application code changes

A workload‑driven approach for view selection in large dimensional datasets

The information explosion the world has witnessed in the last two decades has forced businesses to adopt a data-driven culture for them to be competitive. These data-driven businesses have access to countless sources of information, and face the challenge of making sense of overwhelming amounts of data in a efficient and reliable manner, which implies the execution of read-intensive operations. In the context of this challenge, a framework for the dynamic read-optimization of large dimensional datasets has been designed, and on top of it a workload-driven mechanism for automatic materialized view selection and creation has been developed. This paper presents an extensive description of this mechanism, along with a proof-of-concept implementation of it and its corresponding performance evaluation. Results show that the proposed mechanism is able to derive a limited but comprehensive set of views leading to a drop in query latency ranging from 80% to 99.99% at the expense of 13% of the disk space used by the base dataset. This way, the devised mechanism enables speeding up query execution by building materialized views that match the actual demand of query workloads