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This project develops a Big Data table that allows researchers to query across and among multiple data sources integrated by location. The big table created in this way uses location as the fundamental linkage between data sets. This is the power of geospatial analysis and forms the foundation for the development and interaction with the Health Context Table. The approach utilizes a dense point file...
A kernel or mini-app is a self-contained small application that retains certain characteristics of the original application [7]. Working on a kernel or mini-app in the place of the original application can dramatically reduce the resources and effort required for performing software tasks such as performance optimization and porting to new platforms. However, using kernel as a proxy is based on the...
Traffic classification allows network operators to gain important insights to better characterize packet flows, enabling fundamental applications such as traffic engineering, network analytics and Quality of Service (QoS) enforcing. A common approach adopted for flow classification is based on Deep Packet Inspection (DPI): all the traffic is processed by a middlebox whose task is the association of...
Avionics systems, along with their internal hardware and software components interfaces, must be well defined and specified (e.g., unambiguous, complete, verifiable, consistent, and traceable specification). Such a specification is usually written in the form of an Interface Control Document (ICD), and represents the cornerstone of the avionics system integration activities. However, there is no commonly...
The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed...
a recent trend in intrusion detection is toward utilizing knowledge-based IDSs. Knowledge-based IDSs store knowledge about cyber-attacks and possible vulnerabilities and use this knowledge to guide the process of attack prediction. One significant limitation of knowledge-based IDSs is the lack of contextual information and domain knowledge used to detect attacks. Contextual information is not only...
The advent of social media in education has the potential to foster collaborative learning. Exploring students' interactions on the social media tools is an important research direction, which could bring an insight into the collaborative learning process. Therefore, our aim is to propose a conceptual framework for knowledge extraction and visualization from a social media-based learning environment...
Failing to identify multi-word expression (MWE) may cause serious problems for many Natural Language Processing (NLP) tasks. Previous approaches heavily depend on language specific knowledge and pre-existing natural language processing (NLP) tools. However, many languages (including Chinese language) have less such resources and tools compared to English. An automatically learn effective features...
Context: A key issue when dealing with the generalization threat of software engineering experiments is to use different subject types. Objective: In this paper, we aim to investigate which subject types are used in experiments and their impact on results. Method: We have performed a systematic mapping study by manually searching experiments published from January 2014 to June 2016 in six leading...
This technical briefing provides an overview of how quantitative empirical research methods can be combined with qualitative ones generating the family of empirical software engineering approaches known as mixed-methods. The ultimate aim of such mixed-methods is supporting cause-effect claims combining multiple data types, sources and analyses that provide software practitioners and academicians solid...
In model driven engineering (MDE), meta-models are the central artifacts. As a complement, the Object Constraint Language (OCL) is a language used to express constraints and operations on meta-models. The Eclipse Modeling Framework (EMF) provides an implementation of OCL, enabling OCL-annotated meta-models. Existing empirical studies of the OCL have been conducted on small collections of data. To...
Emotional arousal increases activation and performance but may also lead to burnout in software development. We present the first version of a Software Engineering Arousal lexicon (SEA) that is specifically designed to address the problem of emotional arousal in the software developer ecosystem. SEA is built using a bootstrapping approach that combines word embedding model trained on issue-tracking...
The prediction of the academic success of a student, the change of success according to causes and processes, and the examination of the consequences of this change are a general research topic that deals with many disciplines from different disciplines. Using the approach in this study, patterns of the subset of the data set were obtained by using methods of finding frequently repeated sub-graphs,...
In this paper we present our experience during design, development, and pilot deployments of a data-driven machine learning based application maintenance solution. We implemented a proof of concept to address a spectrum of interrelated problems encountered in application maintenance projects including duplicate incident ticket identification, assignee recommendation, theme mining, and mapping of incidents...
In this paper, we propose learning analytic tasks to understand the learning process in a smart classroom. Learning analytics can extract knowledge from a course to better understand students and their learning processes. The learning analytic tasks must evaluate different aspects in the course: the teaching and learning process, the student performance, and the pedagogical practices, among other...
In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising...
We present SIRUM: a system for Scalable Informative RUle Mining from multi-dimensional data. Informative rules have recently been studied in several contexts, including data summarization, data cube exploration and data quality. The objective is to produce a small set of rules (patterns) over the values of the dimension attributes that provide the most information about the distribution of a numeric...
The paper proposes a knowledge-based framework for mobile autonomous robots. It exploits data annotation for semantic-based context description. High-level event/situation detection and action decision are performed through a semantic matchmaking approach, supporting approximate matches and relevance-based ranking. The framework was fully implemented in a prototype built with off-the-shelf components,...
The affordability of DNA sequencing has led to unprecedented volumes of genomic data. These data must be stored, processed, and analyzed. The most popular format for genomic data is the SAM format, which contains information such as alignment, quality values, etc. These files are large (on the order of terabytes), which necessitates compression. In this work we propose a new reference-based compressor...
Behavioral and targeted profiling of users is an important task in marketing and in the advertising industry. Being able to match a given user profile to an advertising that leads to effective purchases is challenging because of a very tiny proportion of users willing to purchase goods and thus monetize the advertising. With such proportions being less than one percent of the overall user population,...
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