769 research outputs found
A database system with amnesia
Big Data comes with huge challenges. Its volume and velocity makes handling, curating, and analytical processing a costly affair. Even to simply “look at” the data within an a priori defined budget and with a guaranteed interactive response time might be impossible to achieve. Commonly applied scale-out approaches will hit the technology and monetary wall soon, if not done so already. Likewise, blindly rejecting data when the channels are full, or reducing the data resolution at the source, might lead to loss of valuable observations. An army of well-educated database administrators or full software stack architects might deal with these challenges albeit at substantial cost. This calls for a mostly knobless DBMS with a fundamental change in database management. Data rotting has been proposed as a direction to find a solution [10, 11]. For the sake of storage management and responsiveness, it lets the DBMS semi-autonomously rot away data. Rotting is based on the systems own unwillingness to keep old data as easily accessible as fresh data. This paper sheds more light on the opportunities and potential impacts of this radical departure in data management. Specifically, we study the case where a DBMS selectively forgets tuples (by marking them inactive) under various amnesia scenarios and with different implementation strategies. Our ultimate goal is to use the findings of this study to morph an existing data management engine to serve demanding big data scientific applications with well-chosen built-in data a
AnyDB: An Architecture-less DBMS for Any Workload
In this paper, we propose a radical new approach for scale-out distributed
DBMSs. Instead of hard-baking an architectural model, such as a shared-nothing
architecture, into the distributed DBMS design, we aim for a new class of
so-called architecture-less DBMSs. The main idea is that an architecture-less
DBMS can mimic any architecture on a per-query basis on-the-fly without any
additional overhead for reconfiguration. Our initial results show that our
architecture-less DBMS AnyDB can provide significant speed-ups across varying
workloads compared to a traditional DBMS implementing a static architecture.Comment: Submitted to 11th Annual Conference on Innovative Data Systems
Research (CIDR 21
bdbms -- A Database Management System for Biological Data
Biologists are increasingly using databases for storing and managing their
data. Biological databases typically consist of a mixture of raw data,
metadata, sequences, annotations, and related data obtained from various
sources. Current database technology lacks several functionalities that are
needed by biological databases. In this paper, we introduce bdbms, an
extensible prototype database management system for supporting biological data.
bdbms extends the functionalities of current DBMSs to include: (1) Annotation
and provenance management including storage, indexing, manipulation, and
querying of annotation and provenance as first class objects in bdbms, (2)
Local dependency tracking to track the dependencies and derivations among data
items, (3) Update authorization to support data curation via content-based
authorization, in contrast to identity-based authorization, and (4) New access
methods and their supporting operators that support pattern matching on various
types of compressed biological data types. This paper presents the design of
bdbms along with the techniques proposed to support these functionalities
including an extension to SQL. We also outline some open issues in building
bdbms.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
Dodrant-Homomorphic Encryption for Cloud Databases using Table Lookup
Users of large commercial databases increasingly want to outsource their database operations to a cloud service providers, but guaranteeing the privacy of data in an outsourced database has become the major obstacle to this move. Encrypting all data solves the privacy issue, but makes many operations on the data impossible in the cloud, unless the service provider has the capacity to decrypt data temporarily. Homomorphic encryption would solve this issue, but despite great and on-going progress, it is still far from being operationally feasible. In 2015, we presented what we now call dodrant-homomorphic encryption, a method that encrypts numeric values deterministically using the additively homomorphic Paillier encryption and uses table lookup in order to implement multiplications. We discuss here the security implications of determinism and discuss options to avoid these pitfalls
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