Fethi A. Rabhi

UNSW Sydney | UNSW · School of Computer Science and Engineering

The frequency of serious epidemics has continued to increase in the last decade. The ability to predict the risk of outbreaks can improve prevention and control. There are few prediction models available, and of these most are manually constructed by human experts. These manual models are affected by the lack of automation and have limitations in d...

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future i...

Big data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platform...

Consumers often get confused to select the best cloud providers from the huge marketplace. The hesitancy of consumers further escalates when multiple service providers offer the same type and quality of services. To deal with such an uncertainty, the decision-makers always combine multiple factors to make an informed choice. Sentiment mining is one...

The lack of a common framework often complicates the process of provider selection and marginal resource allocation decision. The nonlinear relationships among selection criteria greatly impact the decision-making process. The paper address the critical issue by proposing a centralised Quality of Experience (QoE) and Quality of Service (QoS)- CQoES...

Background: Over the past decade, crowdsourcing marketplaces-online exchange platforms which facilitate commercial outsourcing of services-have witnessed a dramatic growth in the number of participants (service providers and customers) and the value of outsourced services. Deciding about the most appropriate provider is a key challenge for customer...

Housing market dynamics have primarily shifted from consumption- to investment-driven in many countries, including Australia. Building on investment theory, we investigated market dynamics by placing investment demand at the center using the error correction model (ECM). We found that house prices, rents, and interest rates are cointegrated in the...

An organisation wishing to conduct data analytics to support day-to-day decision making often needs a system to help analysts represent and maintain knowledge about research variables, datasets or analytical models, and effectively determine the best combination to use when solving the problem at hand. Often, such knowledge is not explicitly captur...

This paper presents a literature survey of how machine learning techniques are being used in the area of electronic financial market trading. It first defines the essential components of an electronic trading system. It then examines some existing research efforts in applying machine learning techniques to the area of electronic trading, examining...

Research around cloud computing has largely been dedicated to ad-dressing technical aspects associated with utilizing cloud services, surveying critical success factors for the cloud adoption, and opinions about its impact on IT functions. Nevertheless, the aspect of process models for the cloud migration has been slow in pace. Several methodologie...

Moving existing legacy systems to cloud platforms is a difficult and high cost process that may involve technical and non-technical resources and challenges. There is evidence that the lack of understanding and preparedness of cloud computing migration underpin many migration failures in achieving organisations goals. The main goal of this article...

Many business processes present in modern enterprises are loosely defined, highly interactive, involve frequent human interventions and coupled with a multitude of abstract entities defined within an enterprise architecture. Further, they demand agility and responsiveness to address the frequently changing business requirements. Traditional busines...

The increasing reliance on data for decision making has led to a number of techniques for automatic knowledge acquisition such as Formal Concept Analysis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge st...

Organisations today collect vast amounts of data which can be used to provide valuable data-driven insights for their business. However, heterogenous data sources, numerous analytical techniques and various data processing tools create difficulties for data scientists in organisations to navigate these resources and use them effectively to unravel...

Enterprises today are presented with a plethora of data, tools and analytics techniques, but lack systems which help analysts to navigate these resources and identify best fitting solutions for their analytics problems. To support enterprise-level data analytics research, this paper presents Research Variable Ontology (RVO), an ontology designed to...

Data Analytics Solution (DAS) engineering often involves multiple tasks from data exploration to result presentation which are applied in various contexts and on different datasets. Semantic modeling based on the open world assumption supports flexible modeling of linked knowledge. The objective of this paper is to review existing techniques that l...

Current studies on financial markets reaction to news show lack of flexibility for conducting news sentiment datasets evaluations. In other words, there is an absence of clear step-by-step guidance for conducting impact analysis studies in various financial contexts. This paper evaluates the proposed News Sentiment Impact Analysis (NSIA) framework...

This paper focuses on the use of knowledge management techniques to help organisations tap into the power of statistical learning when conducting analytics. Its main contribution is in the use of an ontology development process to derive the essential concepts required for an ontology to represent variables of interest and their interrelationships...

Comprehensively describing data analytics requirements is becoming an integral part of developing enterprise information systems. It is a challenging task for analysts to completely elicit all requirements shared by the organization’s decision makers. With a multitude of data available from e-commerce sites, social media and data warehouses selecti...

Purpose Integrating ontologies with process modeling has gained increasing attention in recent years since it enhances data representations and makes it easier to query, store and reuse knowledge at the semantic level. The authors focused on a process and ontology integration approach by extracting the activities, roles and other concepts related t...

The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint and focus and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core...

Commodity OS kernels have broad attack surfaces due to the large code base and the numerous features such as device drivers. For a real-world use case (e.g., an Apache Server), many kernel services are unused and only a small amount of kernel code is used. Within the used code, a certain part is invoked only at runtime while the rest are executed a...

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is...

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is...

Service-Oriented Architecture (SOA) governance is considered a key success factor when using a service-oriented approach for aligning IT to business. However, some organizations misinterpret the role of SOA inside the organization and there is scarce empirical evidence about how SOA governance is applied in practice. This research paper will study...

Massive amounts of data during disaster situations require timely collection and analysis for the emergency team to mitigate the impact of the disaster under challenging social-technical conditions. The absence of Internet or its intermittent and bandwidth-constraint connection in disaster areas may exacerbate and disrupt the data collection proces...

As big data analytics is adapted across multitude of domains and applications there is a need for new platforms and architectures that support analytic solution engineering as a lean and iterative process. In this paper we discuss how different software development processes can be adapted to data analytic process engineering, incorporating service...

Commodity OS kernels are known to have broad attack surfaces due to the large code base and the numerous features such as device drivers. For a real-world use case (e.g., an Apache Server), many kernel services are unused and only a small amount of kernel code is used. Within the used code, a certain part is invoked only at the runtime phase while...

Research around cloud computing has largely been dedicated to addressing technical aspects associated with utilizing cloud services, surveying critical success factors for the cloud adoption, and opinions about its impact on IT functions. Nevertheless, the aspect of process models for the cloud migration has been slow in pace. Several methodologies...

Many business processes present in modern enterprises are loosely defined, highly interactive, involve frequent human interventions. They are coupled with a multitude of abstract entities defined within an enterprise architecture. Further, they demand agility and responsiveness to address the frequently changing business requirements. Traditional p...

The ability to understand application performance and pro-actively manage system state is becoming increasingly important as infrastructure services move towards commoditisation models such as cloud computing. The complexity of systems being monitored in large corporations means that detailed component-by-component analysis and/or simulation of beh...

Most big data analytics research is scattered across multiple disciplines such as applied statistics, machine learning, language technology or databases. Little attention has been paid to aligning big data solutions with end-user’s mental models for conducting exploratory and predictive data analysis. We are particularly interested in the way domai...

Moving existing legacy systems to cloud platforms is a difficult and high cost process that may involve technical and non-technical resources and challenges. There is evidence that the lack of understanding and preparedness of cloud computing migration underpin many migration failures in achieving organi-sations' goals. The main goal of this articl...

With the growth of data available for analysis, people in many sectors are looking for tools to assist them in collating and visualising patterns in that data. We have developed an event based visualisation system which provides an interactive interface for experts to filter and analyse data. We show that by thinking in terms of events, event hiera...

Over the past decade, several sophisticated analytic techniques such as machine learning, neural networks, and predictive modelling have evolved to enable scientists to derive insights from data. Data Science is characterised by a cycle of model selection, customization and testing, as scientists often do not know the exact goal or expected results...

News analysis activities have been the focus of many research studies across various life domains. So often, the goal of these studies is to automatically, analyze the meaning of news, and to gauge their impact on a particular domain. In this paper, we focus on studying sentiment analysis impact, on financial markets. Current studies, lack systemat...

Real-time analytics is a special kind of Big Data analytics in which data elements are required to be processed and analyzed as they arrive in real time. It is important in situations where real-time processing and analysis can deliver important insights and yield business value. This chapter provides an overview of current processing and analytics...

Many organisations are currently moving their legacy systems to the cloud as it offers reduced cost, improved operational efficiency, on-demand, and pay-as-you-go service models. While any cloud migration scenario to make legacies cloud-enabled may have their own characteristics, there is no universally superior or applicable method for all scenari...

Event-based analytics is increasingly gaining prominence in business and social applications. Despite the availability of many solutions specializing in event processing systems (e.g. CEP technology), there is currently no commonly agreed way of describing event and event pattern types, and thus no standardized method for interchange of event patte...

There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web,...

Research around cloud computing has largely been dedicated to addressing technical aspects associated with utilizing cloud services, surveying critical success factors for the cloud adoption, and opinions about its impact on IT functions. Nevertheless, the aspect of process models for the cloud migration has been slow in pace. Several methodologies...

There is a general consensus that SOA benefits could be reached but it is unclear how to achieve this. Research shows that the problems with SOA governance in practice are among the major reasons of SOA failures. Based on a literature review, this study first proposes a list of SOA aspects to be considered when implementing SOA governance. By adopt...

The academic literature on Service Oriented Architecture (SOA) governance is based on theories and assumptions rather than practices, whereas the SOA governance frameworks proposed by Information Technology (IT) vendors are made to suit their products. Research shows that the problems with SOA governance in practice are among the major reasons of S...

Event data analysis is becoming increasingly of interest to academic researchers looking for patterns in the data. Unlike domain experts working in large companies who have access to IT staff and expensive software infrastructures, researchers find it harder to efficiently manage their event data analysis by themselves. Particularly, user-driven ru...

Firms are seeking new avenues for organizational agility in response to rapidly changing market environments. Research literature in strategic management indicates that firms may gain a competitive advantage in such situations by concentrating on their dynamic capabilities-i.e., product flexibility and agility in organizational transformation in re...

One of the main activities in data-intensive science is data analysis. Although there are many popular technologies that can assist scientists in various isolated aspects of data analysis, supporting analysis processes in holistic ways that promote system interoperability, integration and automation, as well as scientific reproducibility and effici...

Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and networking equipment) and software (databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business c...

We have developed an event based visualisation model for analysing patterns between news story data and stock prices. Visual analytics systems generally show a direct mapping from data to visualisation. We show that by inserting an intermediate step, which models an expert manipulating data, we can provide unique results that display patterns withi...

The availability of novel information may significantly affect the evolution of asset prices. Nonetheless, investors are influenced not only by the quantitative facts but also by the textual content of news disclosures. In this paper, we examine whether news reception in the oil market is time-dependent using a rolling window regression. Our findin...

The impact of financial news on financial markets has been studied extensively. A number of news sentiment scoring techniques are being widely used in research and industry. However, results from sentiment studies are hard to interpret contextual and sentiment related parameters change. Sometimes, the conditions which lead to the results are not fu...

Driven by the growth and availability of vast amounts of financial market data, financial studies are becoming increasingly of interest to finance researchers. However, financial market data is normally huge in amount and with data quality issues especially time-related ones, which renders it extremely difficult to generate reliable results and get...

Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers, data processing frameworks, etc.) platforms. Application services hosted on single/multiple cloud provider platforms have diverse characteristics that require extensive m...

The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously...

This research provides a theoretical conceptualization of SOA governance aspects that can be used to assess SOA governance practices and provide guidance to improve them. The review of IT and SOA governance shows that there are conflicting claims and inconsistency in the literature concerning the role of SOA governance. Moreover, there is no empiri...

Event data analysis is becoming increasingly of interest to academic researchers looking for patterns in the data, contributing to the emergence and popularity of a new field called "data intensive science". Unlike domain experts working in large companies which have access to IT staff and expensive software infrastructure, researchers find it hard...

Cloud monitoring activity involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized resources (e.g., VM, storage, network, appliances, etc.), the physical resources they share, the applications running on them and data hosted on them. Applications and resources configuration in cloud computing environment is quite...

Research has shown that the general health and oral health of an individual are closely related. Accordingly, current practice of isolating the information base of medical and oral health domains can be dangerous and detrimental to the health of the individual. However, technical issues such as heterogeneous data collection and storage formats, lim...

The volume, velocity and variety of data generated today require special techniques and technologies for analysis and inferencing. These challenges are significantly pronounced within healthcare where data is being generated exponentially from biomedical research and electronic patient records. Moreover, with the increasing importance on holistic c...

Though recent research has established the inter-dependencies between several medical and oral health conditions, e-Health systems for medical and oral domains have been designed and implemented to operate independently. Such disparate systems coupled with different data capture and storage formats have led to the formation of medical-dental silos....

Research scientists in data-intensive science use a variety of scientific software applications to support their analyses and processes. To efficiently support the work of these scientists, software applications should satisfy the essential requirements of interoperability, integration, automation, reproducibility, and efficient data handling. Vari...

Literature has repeatedly stressed the importance of the association between the oral health and general health of an individual and for the respective healthcare practitioners to work collaboratively. However, the absence of a knowledge base of the scientific evidence of associations between the two domains of medical and oral health has contribut...

This paper is concerned with data provisioning services (information search, retrieval, storage, etc.) dealing with a large and heterogeneous information repository. Increasingly, this class of services is being hosted and delivered through Cloud infrastructures. Although such systems are becoming popular, existing resource management methods (e.g....

A Case-Based Reasoning (CBR) system for medical diagnosis mimics the way doctors make a diagnosis. Given a new case, its accuracy in practice depends on successful retrieval of similar cases. CBR systems have had some success in dealing with simple diseases because of the robustness of their case base. However, their diagnostic accuracy suffers whe...

Event studies have a long history in academic research and were used in disciplines as diverse as economics, law, information technology, marketing, and finance. One of the main challenges is that the process of undertaking such an event study is complex and many assumptions, trade-offs and design decisions need to be made. Based on Service-Oriente...

The Ad hoc DAta Grid Environment (ADAGE) has been proposed as a framework to support analysis processes for large repositories of ad hoc data. Its use of a service-oriented architecture (SOA) brings the promise of flexibility, as well as enabling domain experts to define their own analysis processes at a high level of abstraction. However, these cl...

In this paper we look at the difficulties which retail investors face to obtain all news which affects companies in their portfolio. We provide a high level overview of available financial news categories and sources, the different research strategies applied to the data, and the technical problems this raises. We propose a service-oriented system...

The paper is concerned with the design of user-centered environments for the exploration of large datasets. The specific focus is on high frequency financial news and market data. In previous work, the ADAGE SOA based framework which allows users to model and execute analysis processes has been proposed. One disadvantage is that although much of a...

This book constitutes the proceedings of the 6th International Workshop on Enterprise Applications and Services in the Finance Industry, FinanceCom 2012, held in Barcelona, Spain, on June 10, 2012. The workshop spans multiple disciplines, including technical, service, economic, sociological, and behavioral sciences. It reflects on technologically e...

Cloud monitoring involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized services (e.g., CPU, storage, network, appliances, etc.), the physical resources they share, and the applications running on them or data hosted on them. Monitoring techniques and services can help a cloud provider or application developer...

Service-oriented architecture (SOA) has gained significant attention as a means of developing flexible and modular systems. Academic studies of SOA as a systems development philosophy abound, and recent industry surveys indicate that most firms are also actively pursuing SOA initiatives. This article uses a rigorous case-study methodology to examin...