24,460 research outputs found
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
A Layered Software Architecture for the Management of a Manufacturing Company
In this paper we describe a layered software architecture in the management of a manufactur-ing company that intensively uses computer technology. Application tools, new and legacy, after the updating, operate in a context of an open web oriented architecture. The software architecture enables the integration and interoperability among all tools that support business processes. Manufacturing Executive System and Text Mining tools are excellent interfaces, the former both for internal production and management processes and the latter for external processes coming from the market. In this way, it is possible to implement, a computer integrated factory, flexible and agile, that immediately responds to customer requirements.ICT, Service Oriented Architecture, Web Services, Computer-Integrated Factory, Application Software
From manuscript catalogues to a handbook of Syriac literature: Modeling an infrastructure for Syriaca.org
Despite increasing interest in Syriac studies and growing digital
availability of Syriac texts, there is currently no up-to-date infrastructure
for discovering, identifying, classifying, and referencing works of Syriac
literature. The standard reference work (Baumstark's Geschichte) is over ninety
years old, and the perhaps 20,000 Syriac manuscripts extant worldwide can be
accessed only through disparate catalogues and databases. The present article
proposes a tentative data model for Syriaca.org's New Handbook of Syriac
Literature, an open-access digital publication that will serve as both an
authority file for Syriac works and a guide to accessing their manuscript
representations, editions, and translations. The authors hope that by
publishing a draft data model they can receive feedback and incorporate
suggestions into the next stage of the project.Comment: Part of special issue: Computer-Aided Processing of Intertextuality
in Ancient Languages. 15 pages, 4 figure
ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime
Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems.
Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements.
Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements.
Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor.
Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in todayâs complex system environments.Peer ReviewedPostprint (author's final draft
Text Mining Infrastructure in R
During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.
ELICA: An Automated Tool for Dynamic Extraction of Requirements Relevant Information
Requirements elicitation requires extensive knowledge and deep understanding
of the problem domain where the final system will be situated. However, in many
software development projects, analysts are required to elicit the requirements
from an unfamiliar domain, which often causes communication barriers between
analysts and stakeholders. In this paper, we propose a requirements ELICitation
Aid tool (ELICA) to help analysts better understand the target application
domain by dynamic extraction and labeling of requirements-relevant knowledge.
To extract the relevant terms, we leverage the flexibility and power of
Weighted Finite State Transducers (WFSTs) in dynamic modeling of natural
language processing tasks. In addition to the information conveyed through
text, ELICA captures and processes non-linguistic information about the
intention of speakers such as their confidence level, analytical tone, and
emotions. The extracted information is made available to the analysts as a set
of labeled snippets with highlighted relevant terms which can also be exported
as an artifact of the Requirements Engineering (RE) process. The application
and usefulness of ELICA are demonstrated through a case study. This study shows
how pre-existing relevant information about the application domain and the
information captured during an elicitation meeting, such as the conversation
and stakeholders' intentions, can be captured and used to support analysts
achieving their tasks.Comment: 2018 IEEE 26th International Requirements Engineering Conference
Workshop
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