Pedro Sampaio

Extending BPMN for Supporting Customer-Facing Service Quality Requirements

Service-oriented computing promises to create flexible business processes and applications on demand by dynamically assembling loosely coupled services within and across organizations. Quality requirements play a central role in service sourcing and, together with Service Level Agreements, facilitate service selection and measurement of service delivery effectiveness. This empowers customers to make better decisions when faced with multiple service offerings and varying service costs. However, existing business process modeling languages provide little support for quality requirements annotation and specification. This paper argues that quality requirements are a central aspect of business process modeling specification, and thus proposes to incorporate time, cost and reliability quality requirements as extensions to the Business Process Modeling Notation (BPMN). These quality requirements are evaluated based on analytical model using reduction rules. An example of online purchasing business process is illustrated to demonstrate the applicability of the proposed approach.

A survey of signature based methods for financial fraud detection

Fraud detection mechanisms support the successful identification of fraudulent system transactions performed through security flaws within deployed technology frameworks while maintaining optimal levels of service delivery and a minimal numbers of false alarms. Knowledge discovery techniques have been widely applied in fraud detection for data analysis and training of supervised learning algorithms to support the extraction of fraudulent account behaviour within static data sets. Escalating costs associated with fraud however have continued to drive the migration towards increasingly proactive methods of fraud detection, to support the real-time screening of transactional data and detection of ambiguous user behaviour prior to transaction completion. This shift in data processing from post to pre data storage significantly reduces the available time within which to evaluate newly arriving system requests and produce an accurate fraud decision, demanding increasingly robust and intelligent user profiling technologies to support advanced fraud detection. This paper provides a comprehensive survey of existing research into account signatures, an innovative account profiling technology which maintains a statistical representation of normal account usage for rapid recalculation in real-time. Fraud detection architectures, processing models and applications to date are critically examined and evaluated with respect to their proactive capabilities for detection of fraud within streaming financial data. Discussion is also presented on challenges which remain within the proactive profiling of account behaviour and future research directions within the signature domain.

ElicitO: a quality ontology-guided NFR elicitation tool

Despite the importance of capturing a precise and complete set of requirements in the requirements engineering stage, there are few tools that adequately support requirements analysis in the process of capturing quality related requirements (non-functional requirements). This paper presents ElicitO, a requirements elicitation tool aimed at empowering requirements analysts with a knowledge repository that helps in the process of capturing precise nonfunctional requirements (NFRs) specifications during elicitation interviews. The approach is based on the application of functional and non-functional domain ontologies (quality ontologies) to underpin the elicitation activities. The tool is used as a memory aid to structure elicitation interviews, guide requirements analysts with regard to the important quality aspects relating to a class of applications, and support the development of precise requirements based on characteristics and metrics available in quality model standards.

Analysis of stock market manipulations using knowledge discovery techniques applied to intraday trade prices

This paper addresses challenges relating to applying data mining techniques to detect stock price manipulations and extends previous results by incorporating the analysis of intraday trade prices in addition to closing prices for the investigation of trade-based manipulations. In particular, this work extends previous results on the topic by analysing empirical evidence in normal and manipulated hourly data and the particular characteristics of intraday trades within suspicious hours. Furthermore, the analytical models described in this paper reinforce the results of previous market manipulation studies that are based on traditional statistical and econometrical methods providing an alternative portfolio of methods and techniques originating from the data mining and knowledge discovery areas. With the application of the analytical approach described in this paper, it is possible to identify new fraud manipulation pattern characteristics encoded as decision trees which can be readily employed in fraud detection systems. The paper also proposes a number of policy recommendations towards increasing the effectiveness of the operational processes executed by stock exchange fraud departments and regulatory authorities.

Deductive Object-Oriented Database Systems: A Survey

Deductive object-oriented databases (DOODs) seek to combine the complementary benefits of the deductive and the object-oriented paradigms in the context of databases. Research into DOODs has now been taking place for almost ten years, and a significant number of designs and implementations have been developed. This paper categorises proposals for DOODs, based on the language design strategy pursued, and compares the resulting systems in terms of the support provided for specific deductive and object-oriented features. It is shown how comprehensive proposals have emerged from significantly different design strategies, and it is argued that research on DO ODs is now quite mature, in that consensus is emerging on the capabilities that it is appropriate for DOODs to support.

Towards a Proactive Fraud Management Framework for Financial Data Streams

Effective and efficient fraud prevention is a core capability required from financial institutions towards detecting and minimizing losses due to unlawful transactions. With the ubiquitous availability of unmanned customer interaction channels (e.g., Internet and mobile banking), the challenge of controlling fraud has increased substantially demanding fraud management frameworks capable of providing fraud analysts with effective mechanisms for defining fraud policies, and system architectures for large scale real-time screening of click stream data, arising from multiple channels at differing time windows. In this paper we describe a fraud management framework encompassing a rule-based financial fraud modelling language (FFML) for conceptual level modelling and validation of fraud policies and a fraud prevention architecture based on implementing fraud policies using StreamSQL, a novel and emerging standard for processing real-time data streams. A key element of the framework is the attempt to detect fraud proactively, blocking transactions encompassing suspicious click stream patterns. The framework described in this paper is being developed as an integral part of the FSA compliance program within the SpartaPay payment gateway, a multi-channel financial platform for managing micropayments.

Incorporating the timeliness quality dimension in internet query systems

Internet Query Systems (IQS) are information systems used to query the World Wide Web by finding data sources relevant to a given query and retrieving data taking into account issues such as the unpredictability of access and transfer rates, infinite streams of data, and the ability to produce partial results. Despite the wide availability of research focusing on query processing for IQS, there are surprisingly few contributions addressing data quality issues such as timeliness and accuracy of data resulting from Internet query processing. This paper provides an overview of an ongoing research effort to extend IQS with a data quality component to ensure timeliness of data resulting from Internet query processing. In particular, we illustrate the quality model, data source layer design and the quality aware algebraic query processing framework adopted in our implementation effort.

On Achieving Energy Efficiency and Reducing CO 2 Footprint in Cloud Computing

With the increasing popularity of the cloud computing model and rapid proliferation of cloud infrastructures there are increasing concerns about energy consumption and consequent impact of cloud computing as a contributor to global CO2 emissions. To date, little is known about how to incorporate energy consumption and CO2 concerns into cloud application development and deployment decision models. In this respect, this paper describes an eco-aware approach that relies on the definition, monitoring and utilization of energy and CO2 metrics combined with the use of innovative application scheduling and runtime adaptation techniques to optimize energy consumption and CO2 footprint of cloud applications as well as the underlying infrastructure. The eco-aware approach involves measuring or quantifying the energy consumption and CO2 at different levels of cloud computing, using that information to create scheduling and adaptation techniques that contribute towards reducing the energy consumption and CO2 emissions, and finally testing and validating the developed solutions in a multi-site cloud environment with the help of challenging case study applications. The experimental and validation results show the potential of the eco-aware approach to significantly reduce the CO2 footprint and consequent environmental impact of cloud applications.

Modeling of privacy-aware business processes in BPMN to protect personal data

This paper proposes a privacy-aware business process modeling framework supporting reasoning and enforcement of privacy constraints. The BPMN notation is extended to incorporate visual constructs for modeling privacy requirements. Moreover, we describe Semantic Web Rule Language constructs to represent the semantics of the privacy-aware extensions to BPMN and enable the use of reasoning tools that support the verification and enforcement of privacy constraints during run-time. To analyze the potential applicability of the proposed framework we describe an airport emergency system scenario where we illustrate how the privacy-aware extension facilitates the modeling of privacy of personal data requirements in a challenging application domain.

A requirements elicitation framework and tool for sourcing business-IT aligned e-services

This paper describes a multiple perspectives goal-oriented requirements elicitation framework aimed at identifying IT service requirements for service sourcing in a service-oriented software marketplace (E-services marketplace). The framework is based on decomposing strategic goals into tactical and operational IT goals adopting a multiple perspectives elicitation approach that facilitates business-IT alignment in the elicitation/mapping of the service requirements and in the choice of the target service offerings. The framework is applicable in a scenario where there is a marketplace of software service providers (e.g. ASPs or web service yellow pages) and the sourcing organization is interested in developing service requirements and searching for services available in the marketplace that are aligned with the organizational goals. The framework is supported by a tool that helps the requirements analyst in the process of developing multiple perspectives service goal trees and also in the process of matching service goals to keywords describing service offerings available in the e-Service marketplace.