Over the past two decades, we have experienced major technology disruptions on multiple fronts, none bigger than the emergence of cloud computing, which has led to fundamental changes in how database software is architected. We are seeing several new ...

Virtually all of today's Big Data systems are passive in nature. Here we describe a project to shift Big Data platforms from passive to active. We detail a vision for a scalable system that can continuously and reliably capture Big Data to enable timely ...

In parallel database systems the partitioning methods considered in current researches are static. This research paper presents a partitioning method to divide the database relations into dynamic horizontal partitions. Every partition contains some ...

Recently, Amazon has announced Redshift, a Parallel-Database-as-a Service (PDaaS). Redshift adopts the "virtual cluster" approach to implement multitenancy, which has the merit of hard isolation among tenants (i.e., tenants do not interfere even when ...

Palomar Transient Factory is a comprehensive detection system for the identification and classification of transient astrophysical objects. The central piece in the identification pipeline is represented by an automated classifier that distinguishes ...

Online aggregation provides estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution. When coupled with parallel ...

Recently, massively parallel processing relational database systems (MPPDBs) have gained much momentum in the big data analytic market. With the advent of hosted cloud computing, we envision that the offering of MPPDB-as-a-Service (MPPDBaaS) will become ...

Iterative algorithms occur in many domains of data analysis, such as machine learning or graph analysis. With increasing interest to run those algorithms on very large data sets, we see a need for new techniques to execute iterations in a massively ...

A description and discussion of the SciDB database management system focuses on lessons learned, application areas, performance comparisons against other solutions, and additional approaches to managing data and complex analytics.

Cogset is a generic and efficient engine for reliable storage and parallel processing of distributed data sets. It supports a number of high-level programming interfaces, including a MapReduce interface compatible with Hadoop. In this paper, we present ...

In the parallel pipelined filter ordering problem, we are given a set of n filters that run in parallel. The filters need to be applied to a stream of elements, to determine which elements pass all filters. Each filter has a rate limit r_i on the number ...

The sizes of databases have seen exponential growth in the past, and such growth is expected to accelerate in the future, with the steady drop in storage cost accompanied by a rapid increase in storage capacity. Many years ago, a terabyte database was ...

Like all open standards, the Predictive Model Markup Language (PMML) enables interoperability and portability in the world of data mining and predictive analytics. This means that models developed in any environment and tool set can be deployed and used ...

We present the design, implementation, and evaluation of ArrayStore, a new storage manager for complex, parallel array processing. ArrayStore builds on prior work in the area of multidimensional data storage, but considers the new problem of supporting ...

Shared-nothing and shared-disk are the two most common storage architectures of parallel databases in the past two decades. Both two types of systems have their own merits for different applications. However, there are no much efforts in investigating ...

As the market demand for analyzing data sets of increasing variety and scale continues to explode, the software options for performing this analysis are beginning to proliferate. No fewer than a dozen companies have launched in the past few years that ...

The rapid and continuing increase in available high-performance computing resources has driven simulation-based science in two directions. First, the simulations themselves are growing more complex, whether in the fidelity of the models, spatiotemporal ...

The state of the art commercial query optimizers employ cost-based optimization and exploit dynamic programming (DP) to find the optimal query execution plan (QEP) without evaluating redundant sub-plans. The number of alternative QEPs enumerated by the ...

Much has been said about processing efficiently data in parallel database servers, and some data warehouse applications must process in the order of tens to hundreds of Gigabytes efficiently. Yet, there is no effective approach targeted at using non-...

Commercial enterprise data warehouses are typically implemented on parallel databases due to the inherent scalability and performance limitation of a serial architecture. Queries used in such large data warehouses can contain complex predicates as well ...