The OTree: multidimensional indexing with efficient data sampling for HPC

Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability.Peer ReviewedPostprint (author's final draft

A Survey on Array Storage, Query Languages, and Systems

Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.Comment: 44 page

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

A Backend Framework for the Efficient Management of Power System Measurements

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.Comment: Published in Electric Power Systems Research (2016), not available ye

Towards Knowledge in the Cloud

Knowledge in the form of semantic data is becoming more and more ubiquitous, and the need for scalable, dynamic systems to support collaborative work with such distributed, heterogeneous knowledge arises. We extend the “data in the cloud” approach that is emerging today to “knowledge in the cloud”, with support for handling semantic information, organizing and finding it efficiently and providing reasoning and quality support. Both the life sciences and emergency response fields are identified as strong potential beneficiaries of having ”knowledge in the cloud”