9,420 research outputs found
The use of alternative data models in data warehousing environments
Data Warehouses are increasing their data volume at an accelerated rate; high disk
space consumption; slow query response time and complex database administration are
common problems in these environments. The lack of a proper data model and an
adequate architecture specifically targeted towards these environments are the root
causes of these problems.
Inefficient management of stored data includes duplicate values at column level and
poor management of data sparsity which derives from a low data density, and affects
the final size of Data Warehouses. It has been demonstrated that the Relational Model
and Relational technology are not the best techniques for managing duplicates and data
sparsity.
The novelty of this research is to compare some data models considering their data
density and their data sparsity management to optimise Data Warehouse environments.
The Binary-Relational, the Associative/Triple Store and the Transrelational models
have been investigated and based on the research results a novel Alternative Data
Warehouse Reference architectural configuration has been defined.
For the Transrelational model, no database implementation existed. Therefore it was
necessary to develop an instantiation of it’s storage mechanism, and as far as could be
determined this is the first public domain instantiation available of the storage
mechanism for the Transrelational model
Quality measures for ETL processes: from goals to implementation
Extraction transformation loading (ETL) processes play an increasingly important role for the support of modern business operations. These business processes are centred around artifacts with high variability and diverse lifecycles, which correspond to key business entities. The apparent complexity of these activities has been examined through the prism of business process management, mainly focusing on functional requirements and performance optimization. However, the quality dimension has not yet been thoroughly investigated, and there is a need for a more human-centric approach to bring them closer to business-users requirements. In this paper, we take a first step towards this direction by defining a sound model for ETL process quality characteristics and quantitative measures for each characteristic, based on existing literature. Our model shows dependencies among quality characteristics and can provide the basis for subsequent analysis using goal modeling techniques. We showcase the use of goal modeling for ETL process design through a use case, where we employ the use of a goal model that includes quantitative components (i.e., indicators) for evaluation and analysis of alternative design decisions.Peer ReviewedPostprint (author's final draft
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Toward an Engineering Discipline of Warehouse Design
Warehouses today are complex dynamic engineered systems, incorporating automation, mechanization, equipment, fixtures, computers, networks, products and people, and they can support the flow of tens or hundreds of thousands of different items to enable fulfilling thousands or tens of thousands of orders daily. In that sense, they represent a design challenge that is not terribly different from the design of other complex dynamic engineered systems, such as a modern passenger airplane, an automobile, or a unique building. What is different is that the design of these other complex dynamic engineered systems typically follows some engineering design discipline. Here, we argue for the development of a corresponding engineering discipline of warehouse design
Near-Memory Address Translation
Memory and logic integration on the same chip is becoming increasingly cost
effective, creating the opportunity to offload data-intensive functionality to
processing units placed inside memory chips. The introduction of memory-side
processing units (MPUs) into conventional systems faces virtual memory as the
first big showstopper: without efficient hardware support for address
translation MPUs have highly limited applicability. Unfortunately, conventional
translation mechanisms fall short of providing fast translations as
contemporary memories exceed the reach of TLBs, making expensive page walks
common.
In this paper, we are the first to show that the historically important
flexibility to map any virtual page to any page frame is unnecessary in today's
servers. We find that while limiting the associativity of the
virtual-to-physical mapping incurs no penalty, it can break the
translate-then-fetch serialization if combined with careful data placement in
the MPU's memory, allowing for translation and data fetch to proceed
independently and in parallel. We propose the Distributed Inverted Page Table
(DIPTA), a near-memory structure in which the smallest memory partition keeps
the translation information for its data share, ensuring that the translation
completes together with the data fetch. DIPTA completely eliminates the
performance overhead of translation, achieving speedups of up to 3.81x and
2.13x over conventional translation using 4KB and 1GB pages respectively.Comment: 15 pages, 9 figure
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