11,444 research outputs found
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Innovative Evaluation System – IESM: An Architecture for the Database Management System for Mobile Application
As the mobile applications are constantly facing a rapid development in the recent years especially in the academic environment such as student response system [1-8] used in universities and other educational institutions; there has not been reported an effective and scalable Database Management System to support fast and reliable data storage and retrieval. This paper presents Database Management Architecture for an Innovative Evaluation System based on Mobile Learning Applications. The need for a relatively stable, independent and extensible data model for faster data storage and retrieval is analyzed and investigated. It concludes by emphasizing further investigation for high throughput so as to support multimedia data such as video clips, images and documents
A Call to Arms: Revisiting Database Design
Good database design is crucial to obtain a sound, consistent database, and -
in turn - good database design methodologies are the best way to achieve the
right design. These methodologies are taught to most Computer Science
undergraduates, as part of any Introduction to Database class. They can be
considered part of the "canon", and indeed, the overall approach to database
design has been unchanged for years. Moreover, none of the major database
research assessments identify database design as a strategic research
direction.
Should we conclude that database design is a solved problem?
Our thesis is that database design remains a critical unsolved problem.
Hence, it should be the subject of more research. Our starting point is the
observation that traditional database design is not used in practice - and if
it were used it would result in designs that are not well adapted to current
environments. In short, database design has failed to keep up with the times.
In this paper, we put forth arguments to support our viewpoint, analyze the
root causes of this situation and suggest some avenues of research.Comment: Removed spurious column break. Nothing else was change
Impliance: A Next Generation Information Management Appliance
ably successful in building a large market and adapting to the changes of the
last three decades, its impact on the broader market of information management
is surprisingly limited. If we were to design an information management system
from scratch, based upon today's requirements and hardware capabilities, would
it look anything like today's database systems?" In this paper, we introduce
Impliance, a next-generation information management system consisting of
hardware and software components integrated to form an easy-to-administer
appliance that can store, retrieve, and analyze all types of structured,
semi-structured, and unstructured information. We first summarize the trends
that will shape information management for the foreseeable future. Those trends
imply three major requirements for Impliance: (1) to be able to store, manage,
and uniformly query all data, not just structured records; (2) to be able to
scale out as the volume of this data grows; and (3) to be simple and robust in
operation. We then describe four key ideas that are uniquely combined in
Impliance to address these requirements, namely the ideas of: (a) integrating
software and off-the-shelf hardware into a generic information appliance; (b)
automatically discovering, organizing, and managing all data - unstructured as
well as structured - in a uniform way; (c) achieving scale-out by exploiting
simple, massive parallel processing, and (d) virtualizing compute and storage
resources to unify, simplify, and streamline the management of Impliance.
Impliance is an ambitious, long-term effort to define simpler, more robust, and
more scalable information systems for tomorrow's enterprises.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
Model-driven performance evaluation for service engineering
Service engineering and service-oriented architecture as an
integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of
measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised
for the empirical performance evaluation of model-driven developed service systems
SoK: Cryptographically Protected Database Search
Protected database search systems cryptographically isolate the roles of
reading from, writing to, and administering the database. This separation
limits unnecessary administrator access and protects data in the case of system
breaches. Since protected search was introduced in 2000, the area has grown
rapidly; systems are offered by academia, start-ups, and established companies.
However, there is no best protected search system or set of techniques.
Design of such systems is a balancing act between security, functionality,
performance, and usability. This challenge is made more difficult by ongoing
database specialization, as some users will want the functionality of SQL,
NoSQL, or NewSQL databases. This database evolution will continue, and the
protected search community should be able to quickly provide functionality
consistent with newly invented databases.
At the same time, the community must accurately and clearly characterize the
tradeoffs between different approaches. To address these challenges, we provide
the following contributions:
1) An identification of the important primitive operations across database
paradigms. We find there are a small number of base operations that can be used
and combined to support a large number of database paradigms.
2) An evaluation of the current state of protected search systems in
implementing these base operations. This evaluation describes the main
approaches and tradeoffs for each base operation. Furthermore, it puts
protected search in the context of unprotected search, identifying key gaps in
functionality.
3) An analysis of attacks against protected search for different base
queries.
4) A roadmap and tools for transforming a protected search system into a
protected database, including an open-source performance evaluation platform
and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac
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