52,528 research outputs found
The Mode of Computing
The Turing Machine is the paradigmatic case of computing machines, but there
are others, such as Artificial Neural Networks, Table Computing,
Relational-Indeterminate Computing and diverse forms of analogical computing,
each of which based on a particular underlying intuition of the phenomenon of
computing. This variety can be captured in terms of system levels,
re-interpreting and generalizing Newell's hierarchy, which includes the
knowledge level at the top and the symbol level immediately below it. In this
re-interpretation the knowledge level consists of human knowledge and the
symbol level is generalized into a new level that here is called The Mode of
Computing. Natural computing performed by the brains of humans and non-human
animals with a developed enough neural system should be understood in terms of
a hierarchy of system levels too. By analogy from standard computing machinery
there must be a system level above the neural circuitry levels and directly
below the knowledge level that is named here The mode of Natural Computing. A
central question for Cognition is the characterization of this mode. The Mode
of Computing provides a novel perspective on the phenomena of computing,
interpreting, the representational and non-representational views of cognition,
and consciousness.Comment: 35 pages, 8 figure
Performance issues in mid-sized relational database machines
Relational database systems have provided end users and application programmers with an improved working environment over older hierarchial and networked database systems. End users now use interactive query languages to inspect and manage their data. And application programs are easier to write and maintain due to the separation of physical data storage information from the application program itself. These and other benefits do not come without a price however. System resource consumption has long been the perceived problem with relational systems. The additional resource demands usually force computing sites to upgrade existing systems or add additional facilities. One method of protecting the current investment in systems is to use specialized hardware designed specifically for relational database processing. \u27Database Machines\u27 provide that alternative. Since the commercial introduction of database machines in the early 1980\u27s, both software and hardware vendors of relational database systems have claimed superior performance over competing products. Without a STANDARD performance measurement technique, the database user community has been flooded with benchmarks and claims from vendors which are immediately discarded by some competitors as being biased towards a particular system design. This thesis discusses the issues of relational database performance measurement with an emphasis on database machines, however; these performance issues are applicable to both hardware and software systems. A discussion of hardware design, performance metrics, software and database design is included. Also provided are recommended guidelines to use in evaluating relational database systems in lieu of a standard benchmark methodology
Discriminating weight of Cloud Environment in ERP selection assessment
Selection of an ERP, based on its incorporated component parameters, is a significant problem for an industry. A more comprehensive structure of an ERP is a three tier ERP. A cloud database is a cloud computing service. Database has scalability, and makes underlying virtual machine instances to run a database on such virtual machines. Cloud databases which are relational as opposed to non relational or No SQL databases, imply that SQL databases can run in the cloud with a virtual machine or as a service. Cloud computing architecture is a set of components and subcomponents required for cloud computing. A front end platform set like fat client, thin client, mobile device, Back end platforms like servers, storage, a cloud based delivery, A network such as internet, intranet, Inter cloud connectivity setup are a combined blend that together make a Cloud framework. The paper aims to discriminate availing an ERP in Cloud or Non Cloud framework mode and analyzing pros and cons in both modes
CREOLE: a Universal Language for Creating, Requesting, Updating and Deleting Resources
In the context of Service-Oriented Computing, applications can be developed
following the REST (Representation State Transfer) architectural style. This
style corresponds to a resource-oriented model, where resources are manipulated
via CRUD (Create, Request, Update, Delete) interfaces. The diversity of CRUD
languages due to the absence of a standard leads to composition problems
related to adaptation, integration and coordination of services. To overcome
these problems, we propose a pivot architecture built around a universal
language to manipulate resources, called CREOLE, a CRUD Language for Resource
Edition. In this architecture, scripts written in existing CRUD languages, like
SQL, are compiled into Creole and then executed over different CRUD interfaces.
After stating the requirements for a universal language for manipulating
resources, we formally describe the language and informally motivate its
definition with respect to the requirements. We then concretely show how the
architecture solves adaptation, integration and coordination problems in the
case of photo management in Flickr and Picasa, two well-known service-oriented
applications. Finally, we propose a roadmap for future work.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499
Parallel Processing of Large Graphs
More and more large data collections are gathered worldwide in various IT
systems. Many of them possess the networked nature and need to be processed and
analysed as graph structures. Due to their size they require very often usage
of parallel paradigm for efficient computation. Three parallel techniques have
been compared in the paper: MapReduce, its map-side join extension and Bulk
Synchronous Parallel (BSP). They are implemented for two different graph
problems: calculation of single source shortest paths (SSSP) and collective
classification of graph nodes by means of relational influence propagation
(RIP). The methods and algorithms are applied to several network datasets
differing in size and structural profile, originating from three domains:
telecommunication, multimedia and microblog. The results revealed that
iterative graph processing with the BSP implementation always and
significantly, even up to 10 times outperforms MapReduce, especially for
algorithms with many iterations and sparse communication. Also MapReduce
extension based on map-side join usually noticeably presents better efficiency,
although not as much as BSP. Nevertheless, MapReduce still remains the good
alternative for enormous networks, whose data structures do not fit in local
memories.Comment: Preprint submitted to Future Generation Computer System
Learning from Multi-View Multi-Way Data via Structural Factorization Machines
Real-world relations among entities can often be observed and determined by
different perspectives/views. For example, the decision made by a user on
whether to adopt an item relies on multiple aspects such as the contextual
information of the decision, the item's attributes, the user's profile and the
reviews given by other users. Different views may exhibit multi-way
interactions among entities and provide complementary information. In this
paper, we introduce a multi-tensor-based approach that can preserve the
underlying structure of multi-view data in a generic predictive model.
Specifically, we propose structural factorization machines (SFMs) that learn
the common latent spaces shared by multi-view tensors and automatically adjust
the importance of each view in the predictive model. Furthermore, the
complexity of SFMs is linear in the number of parameters, which make SFMs
suitable to large-scale problems. Extensive experiments on real-world datasets
demonstrate that the proposed SFMs outperform several state-of-the-art methods
in terms of prediction accuracy and computational cost.Comment: 10 page
Search Based Software Engineering in Membrane Computing
This paper presents a testing approach for kernel P Systems (kP systems),
based on test data generation for a given scenario. This method uses Genetic Algorithms
to generate the input sets needed to trigger the given computation steps
An Approach to Ad hoc Cloud Computing
We consider how underused computing resources within an enterprise may be
harnessed to improve utilization and create an elastic computing
infrastructure. Most current cloud provision involves a data center model, in
which clusters of machines are dedicated to running cloud infrastructure
software. We propose an additional model, the ad hoc cloud, in which
infrastructure software is distributed over resources harvested from machines
already in existence within an enterprise. In contrast to the data center cloud
model, resource levels are not established a priori, nor are resources
dedicated exclusively to the cloud while in use. A participating machine is not
dedicated to the cloud, but has some other primary purpose such as running
interactive processes for a particular user. We outline the major
implementation challenges and one approach to tackling them
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