Lydia Lau

Multi-tenancy in Cloud Computing

As Cloud Computing becomes the trend of information technology computational model, the Cloud security is becoming a major issue in adopting the Cloud where security is considered one of the most critical concerns for the large customers of Cloud (i.e. governments and enterprises). Such valid concern is mainly driven by the Multi-Tenancy situation which refers to resource sharing in Cloud Computing and its associated risks where confidentiality and/or integrity could be violated. As a result, security concerns may harness the advancement of Cloud Computing in the market. So, in order to propose effective security solutions and strategies a good knowledge of the current Cloud implementations and practices, especially the public Clouds, must be understood by professionals. Such understanding is needed in order to recognize attack vectors and attack surfaces. In this paper we will propose an attack model based on a threat model designed to take advantage of Multi-Tenancy situation only. Before that, a clear understanding of Multi-Tenancy, its origin and its benefits will be demonstrated. Also, a novel way on how to approach Multi-Tenancy will be illustrated. Finally, we will try to sense any suspicious behavior that may indicate to a possible attack where we will try to recognize the proposed attack model empirically from Google trace logs. Google trace logs are a 29-day worth of data released by Google. The data set was utilized in reliability and power consumption studies, but not been utilized in any security study to the extent of our knowledge.

Taming digital traces for informal learning: a semantic-driven approach

Modern learning models require linking experiences in training environments with experiences in the real-world. However, data about real-world experiences is notoriously hard to collect. Social spaces bring new opportunities to tackle this challenge, supplying digital traces where people talk about their real-world experiences. These traces can become valuable resource, especially in ill-defined domains that embed multiple interpretations. The paper presents a unique approach to aggregate content from social spaces into a semantic-enriched data browser to facilitate informal learning in ill-defined domains. This work pioneers a new way to exploit digital traces about real-world experiences as authentic examples in informal learning contexts. An exploratory study is used to determine both strengths and areas needing attention. The results suggest that semantics can be successfully used in social spaces for informal learning – especially when combined with carefully designed nudges.

A priori ontology modularisation in ill-defined domains

Modularisation is crucial to create re-usable and manageable ontologies. The modularisation is usually performed a posteriori, i.e. after the ontology is developed, and has been applied mainly to well-structured domains. With the increasing popularity of social media, Semantic web technologies are moving towards ill-defined domains that involve cognitively-complex processes carried out by humans and require tacit knowledge (e.g. decision-making, sensemaking, interpersonal communication, negotiating, motivating). In such domains, a priori modularisation can enable ontology creation to handle the complexity and the dynamic nature of knowledge. This paper outlines an a priori modularisation methodology for multi-layered development of ontologies in ill-defined domains, including an upper ontology layer, high-level and reusable domain layers, and case-specific layers. The methodology is being applied in several use cases in two EU projects -- Dicode and ImREAL.

Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective

Big data analytics requires technologies to efficiently process large quantities of data. Moreover, especially in decision making, it not only requires individual intellectual capabilities in the analytical activities but also collective knowledge. Very often, people with diverse expert knowledge need to work together towards a meaningful interpretation of the associated results for new insight. Thus, a big data analysis infrastructure must both support technical innovation and effectively accommodate input from multiple human experts. In this chapter, we aim to advance our understanding on the synergy between human and machine intelligence in tackling big data analysis. Sensemaking models for big data analysis were explored and used to inform the development of a generic conceptual architecture as a means to frame the requirements of such an analysis and to position the role of both technology and human in this synergetic relationship. Two contrasting real-world use case studies were undertaken to test the applicability of the proposed architecture for the development of a supporting platform for big data analysis. Reflection on this outcome has further advanced our understanding on the complexity and the potential of individual and collaborative sensemaking models for big data analytics.

Personalised Provenance Reasoning Models and Risk Assessment in Business Systems: A Case Study

As modern information systems become increasingly business- and safety-critical, it is extremely important to improve both the trust that a user places in a system and their understanding of the risks associated with making a decision. This paper presents the STRAPP framework, a generic framework that supports both of these goals through the use of personalised provenance reasoning engines and state-of-art risk assessment techniques. We present the high-level architecture of the framework, and describe the process of systematically modelling system provenance with the W3C PROV provenance data model. We discuss the business drivers behind the concept of personalizing provenance information, and describe an approach to enabling this through a user-adaptive system style. We discuss using data provenance for risk management and treatment in order to evaluate risk levels, and discuss the use of CORAS to develop a risk reasoning engine representing core classes and relationships. Finally, we demonstrate the initial implementation of our personalised provenance system in the context of the Rolls-Royce Equipment Health Management, and discuss its operation, the lessons we have learnt through our research and implementation (both technical and in business), and our future plans for this project.

Exploring exploratory search: a user study with linked semantic data

The maturation of semantic technologies and the growing popularity of the Linked Open Data (LOD) cloud make it possible to expose linked semantic data sets to end users in order to empower a range of analytical tasks taking advantage of knowledge integration and semantic linking. Linked semantic data appears to offer a great potential for exploratory search, which is open-ended, multi-faceted, and iterative in nature. However, there is limited insight into how browsing through linked semantic data sets can support exploratory search. This paper presents a user study with a uni-focal semantic browsing interface for exploratory search through several data sets linked via domain ontologies. The study, which is qualitative and exploratory in nature and uses music as an illustrative domain, examines (i) obstacles and challenges related to user exploratory search in LOD and (ii) the serendipitous learning effect and the role semantics plays in that. The approach and lessons learnt can benefit future human factor studies to evaluate interactive exploration of linked semantic data, as well as technology developers to become aware of issues that have to be addressed in to facilitate exploratory search with LOD.

Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching

Videos can be a powerful medium for acquiring soft skills, where learning requires contextualisation in personal experience and ability to see different perspectives. However, to learn effectively while watching videos, students need to actively engage with video content. We implemented interactive notetaking during video watching in an active video watching system (AVW) as a means to encourage engagement. This paper proposes a systematic approach to utilise learning analytics for the introduction of adaptive intervention - a choice architecture for personalised nudges in the AVW to extend learning. A user study was conducted and used as an illustration. By characterising clusters derived from user profiles, we identify different styles of engagement, such as parochial learning, habitual video watching, and self-regulated learning (which is the target ideal behaviour). To find opportunities for interventions, interaction traces in the AVW were used to identify video intervals with high user interest and relevant behaviour patterns that indicate when nudges may be triggered. A prediction model was developed to identify comments that are likely to have high social value, and can be used as examples in nudges. A framework for interactive personalised nudges was then conceptualised for the case study.

Assisting user browsing over linked data: requirements elicitation with a user study

There are growing arguments that linked data technologies can be utilised to enable user-oriented exploratory search systems for the future Internet. Recently, search over linked data has been studied in different domains and contexts. However, there is still limited insight into how conventional semantic browsers over linked data can be extended to empower exploratory search, which is open-ended, multi-faceted and iterative in nature. Empirical user studies in representative domains can identify problems and elicit requirements for innovative functionality to assist user exploration. This paper presents such an approach --- a user study with a uni-focal semantic data browser over several datasets linked via domain ontologies is used to inform what intelligent features are needed in order to assist exploratory search through linked data. We report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. A semantic signposting approach for extending a semantic data browser is proposed as a way to address the derived requirements.

Deriving group profiles from social media to facilitate the design of simulated environments for learning

Simulated environments for learning are becoming increasingly popular to support experiential learning in complex domains. A key challenge when designing simulated learning environments is how to align the experience in the simulated world with real world experiences. Social media resources provide user-generated content that is rich in digital traces of real world experiences. People comments, tweets, and blog posts in social spaces can reveal interesting aspects of real world situations or can show what particular group of users is interested in or aware of. This paper examines a systematic way to analyze user-generated content in social media resources to provide useful information for learning simulator design. A hybrid framework exploiting Machine Learning and Semantics for social group profiling is presented. The framework has five stages: (1) Retrieval of user-generated content from the social resource (2) Content noise filtration, removing spam, abuse, and content irrelevant to the learning domain; (3) Deriving individual social profiles for the content authors; (4) Clustering of individuals into groups of similar authors; and (5) Deriving group profiles, where interesting concepts suitable for the use in simulated learning systems are extracted from the aggregated content authored by each group. The framework is applied to derive group profiles by mining user comments on YouTube videos. The application is evaluated in an experimental study within the context of learning interpersonal skills in job interviews. The paper discusses how the YouTube-based group profiles can be used to facilitate the design of a job interview skills learning simulator, considering: (1) identifying learning needs based on digital traces of real world experiences; and (2) augmenting learner models in simulators based on group characteristics derived from social media.

An Adaptive Approach to P2P Resource Discovery in Distributed Scientific Research Communities

Resource discovery in a distributed environment is always a challenging issue. It is even more difficult to provide an efficient query routing mechanism while still able to support complex query processing in a decentralised P2P environment. This paper presents an adaptive approach to P2P resource discovery. It separates the routing of queries from query matching mechanism so that an effective combination could be explored. Three properties of scientific research communities provide the grounding for the method: the existence of common interest groups, the willingness to share resources of common interests and the transitive relationship in the sharing behaviour. By exploiting these properties, search queries can be efficiently forwarded to those who are more likely to have the answers to improve the quality of search results and to reduce the network traffic. Experimental results have provided some evidence to confirm the efficiency of this adaptive approach.