174,236 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
Using a Logic Programming Framework to Control Database Query Dialogues in Natural Language
We present a natural language question/answering system to interface the University of Évora databases that uses clarification dialogs in order to clarify user questions. It was developed in an integrated logic programming framework, based on constraint logic programming using the GnuProlog(-cx) language [2,11] and the ISCO framework [1]. The use of this LP framework allows the integration of Prolog-like inference mechanisms with classes and inheritance, constraint solving algorithms and provides the connection with relational databases, such as PostgreSQL. This system focus on the questions’ pragmatic analysis, to handle ambiguity, and on an efficient dialogue mechanism, which is able to place relevant questions to clarify the user intentions in a straightforward manner. Proper Nouns resolution and the pp-attachment problem are also handled.
This paper briefly presents this innovative system focusing on its ability to correctly determine the user intention through its dialogue capability
User Defined Types and Nested Tables in Object Relational Databases
Bernadette Byrne, Mary Garvey, ‘User Defined Types and Nested Tables in Object Relational Databases’, paper presented at the United Kingdom Academy for Information Systems 2006: Putting Theory into Practice, Cheltenham, UK, 5-7 June, 2006.There has been much research and work into incorporating objects into databases with a number of object databases being developed in the 1980s and 1990s. During the 1990s the concept of object relational databases became popular, with object extensions to the relational model. As a result, several relational databases have added such extensions. There has been little in the way of formal evaluation of object relational extensions to commercial database systems. In this work an airline flight logging system, a real-world database application, was taken and a database developed using a regular relational database and again using object relational extensions, allowing the evaluation of the relational extensions.Peer reviewe
Towards a Novel Cooperative Logistics Information System Framework
Supply Chains and Logistics have a growing importance in global economy.
Supply Chain Information Systems over the world are heterogeneous and each one
can both produce and receive massive amounts of structured and unstructured
data in real-time, which are usually generated by information systems,
connected objects or manually by humans. This heterogeneity is due to Logistics
Information Systems components and processes that are developed by different
modelling methods and running on many platforms; hence, decision making process
is difficult in such multi-actor environment. In this paper we identify some
current challenges and integration issues between separately designed Logistics
Information Systems (LIS), and we propose a Distributed Cooperative Logistics
Platform (DCLP) framework based on NoSQL, which facilitates real-time
cooperation between stakeholders and improves decision making process in a
multi-actor environment. We included also a case study of Hospital Supply Chain
(HSC), and a brief discussion on perspectives and future scope of work
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Clinical metagenomics.
Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
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