6,345 research outputs found
Enabling On-Demand Database Computing with MIT SuperCloud Database Management System
The MIT SuperCloud database management system allows for rapid creation and
flexible execution of a variety of the latest scientific databases, including
Apache Accumulo and SciDB. It is designed to permit these databases to run on a
High Performance Computing Cluster (HPCC) platform as seamlessly as any other
HPCC job. It ensures the seamless migration of the databases to the resources
assigned by the HPCC scheduler and centralized storage of the database files
when not running. It also permits snapshotting of databases to allow
researchers to experiment and push the limits of the technology without
concerns for data or productivity loss if the database becomes unstable.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing (HPEC)
conference 2015. arXiv admin note: text overlap with arXiv:1406.492
SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects
With the proliferation of mobile devices and location-based services,
continuous generation of massive volume of streaming spatial objects (i.e.,
geo-tagged data) opens up new opportunities to address real-world problems by
analyzing them. In this paper, we present a novel continuous bursty region
detection problem that aims to continuously detect a bursty region of a given
size in a specified geographical area from a stream of spatial objects.
Specifically, a bursty region shows maximum spike in the number of spatial
objects in a given time window. The problem is useful in addressing several
real-world challenges such as surge pricing problem in online transportation
and disease outbreak detection. To solve the problem, we propose an exact
solution and two approximate solutions, and the approximation ratio is
in terms of the burst score, where is a parameter
to control the burst score. We further extend these solutions to support
detection of top- bursty regions. Extensive experiments with real-world data
are conducted to demonstrate the efficiency and effectiveness of our solutions
Infinite Probabilistic Databases
Probabilistic databases (PDBs) are used to model uncertainty in data in a quantitative way. In the standard formal framework, PDBs are finite probability spaces over relational database instances. It has been argued convincingly that this is not compatible with an open-world semantics (Ceylan et al., KR 2016) and with application scenarios that are modeled by continuous probability distributions (Dalvi et al., CACM 2009).
We recently introduced a model of PDBs as infinite probability spaces that addresses these issues (Grohe and Lindner, PODS 2019). While that work was mainly concerned with countably infinite probability spaces, our focus here is on uncountable spaces. Such an extension is necessary to model typical continuous probability distributions that appear in many applications. However, an extension beyond countable probability spaces raises nontrivial foundational issues concerned with the measurability of events and queries and ultimately with the question whether queries have a well-defined semantics.
It turns out that so-called finite point processes are the appropriate model from probability theory for dealing with probabilistic databases. This model allows us to construct suitable (uncountable) probability spaces of database instances in a systematic way. Our main technical results are measurability statements for relational algebra queries as well as aggregate queries and Datalog queries
- …