172,400 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Recommended from our members
Hybrid process modelling within business process management projects
Business Process Management (BPM) is still an important research topic amongst both academics
and businesses. The recent recession has forced businesses to focus on cost control and efficiency
in order to better cope with the economic downturn. Many companies in this situation turn to BPM
software as a means of improving their efficiency and costs by reducing aspects of the business
such as process lead-times and material costs. In order to identify areas of the business and its
processes which require changing the business will most likely adopt a method of modelling their
business processes. Because of the large number of available techniques decision makers usually
struggle to decide the best approach. Recent literature has also pointed out that prevalent
modelling techniques are designed to serve one specific purpose and may not be capable of
modelling the whole picture. The key relationship between the information systems and the human
behaviour is one example of where existing techniques are biased towards opposite ends of the
scale. This paper proposes the use of a hybrid modelling notation composed of multiple existing
notations in order to bridge this. The hybrid notation was applied to a BPM project at a company
in the construction industry and a case study conducted with its users
Temporal word embeddings for dynamic user profiling in Twitter
The research described in this paper focused on exploring
the domain of user profiling, a nascent and contentious technology which
has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised.
An extensive review of related literature revealed that limited research
has been conducted into how temporal aspects of users can be captured
using user profiling techniques. This, coupled with the notable lack of
research into the use of word embedding techniques to capture temporal
variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could
be modelled based on their use of language. To achieve this, this work
concerned itself with extending an existing implementation of Temporal
Random Indexing to model Twitter users across multiple granularities of
time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe
the evolution of a Twitter userâs interests over time through their use of
language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the
Word2Vec Dynamic Independent Skip-gram model, where it was found
that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles
Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques
Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a userâs interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to
be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning
methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories.
We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that
proposes a new form of interaction between users and digital libraries, where the latter are adapted to users
and their surroundings
Formal models, usability and related work in IR (editorial for special edition)
The Glasgow IR group has carried out both theoretical and empirical work, aimed at giving end users efficient and effective access to large collections of multimedia data
A novel algorithm for dynamic student profile adaptation based on learning styles
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the studentsâ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the studentsâ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify studentsâ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method
Case-based analysis in user requirements modelling for knowledge construction
Context: Learning can be regarded as knowledge construction in which prior knowledge and experience
serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction
has to take place continuously in order to enhance the learnersâ competence in a competitive working
environment. As the information consumers, the individual users demand personalised information provision
which meets their own specific purposes, goals, and expectations.
Objectives: The current methods in requirements engineering are capable of modelling the common
userâs behaviour in the domain of knowledge construction. The usersâ requirements can be represented
as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis
needs to be enhanced so that personalised information provision can be tackled and modelled. However,
there is a lack of suitable modelling methods to achieve this end. This paper presents a new
ontological method for capturing individual userâs requirements and transforming the requirements onto
personalised information provision specifications. Hence the right information can be provided to the
right user for the right purpose.
Method: An experiment was conducted based on the qualitative method. A medium size of group of users
participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content.
The results were used as the feedback for the improvement.
Result: The research work has produced an ontology model with a set of techniques which support the
functions for profiling userâs requirements, reasoning requirements patterns, generating workflow from
norms, and formulating information provision specifications.
Conclusion: The current requirements engineering approaches provide the methodical capability for
developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further
enhance the RE approaches for modelling the individual userâs needs and discovering the userâs
requirements
- âŠ