Daniele Barone

Strategic business modeling: representation and reasoning

Business intelligence (BI) offers tremendous potential for business organizations to gain insights into their day-to-day operations, as well as longer term opportunities and threats. However, most of today’s BI tools are based on models that are too much data-oriented from the point of view of business decision makers. We propose an enterprise modeling approach to bridge the business-level understanding of the enterprise with its representations in databases and data warehouses. The business intelligence model (BIM) offers concepts familiar to business decision making—such as goals, strategies, processes, situations, influences, and indicators. Unlike many enterprise models which are meant to be used to derive, manage, or align with IT system implementations, BIM aims to help business users organize and make sense of the vast amounts of data about the enterprise and its external environment. In this paper, we present core BIM concepts, focusing especially on reasoning about situations, influences, and indicators. Such reasoning supports strategic analysis of business objectives in light of current enterprise data, allowing analysts to explore scenarios and find alternative strategies. We describe how goal reasoning techniques from conceptual modeling and requirements engineering have been applied to BIM. Techniques are also provided to support reasoning with indicators linked to business metrics, including cases where specifications of indicators are incomplete. Evaluation of the proposed modeling and reasoning framework includes an on-going prototype implementation, as well as case studies.

Composite indicators for business intelligence

Business organizations continuously monitor their environments, looking out for opportunities and threats that may help/hinter the fulfilment of their objectives. We are interested in strategic business models that support such governance activities. In this paper, we focus on the concept of composite indicator and show how it can be used as basic building block for strategic business models that support evaluation and decision-making. The main results of this paper include techniques and algorithms for deriving values for composite indicators, when the relationship between a composite indicator and its component indicators cannot be fully described using well-defined mathematical functions.

Reasoning with Key Performance Indicators

Business organizations continuously monitor their environments, looking out for opportunities and threats that may help/hinder the fulfilment of their objectives. We are interested in strategic business models that support such governance activities. In this paper, we focus on the concept of composite indicator and show how it can be used as basic building block for strategic business models that support evaluation and decision-making. The main results of this paper include techniques and algorithms for deriving values for composite indicators, when the relationship between a composite indicator and its component indicators cannot be fully described using well-defined mathematical functions. Evaluation of our proposal includes an implemented Eclipse-based prototype tool supporting these techniques and two ongoing case studies.

Strategic models for business intelligence

Business Intelligence (BI) promises a range of technologies for using information to ensure compliance to strategic and tactical objectives, as well as government laws and regulations. These technologies can be used in conjunction with conceptual models of business objectives, processes and situations (aka business schemas) to drive strategic decision-making about opportunities and threats. This paper focuses on three key concepts for strategic business models - situation, influence and indicator - and how they are used, in the context of goal modeling, to build and analyze business schemas based on goal and probabilistic reasoning techniques.

Business intelligence modeling in action: a hospital case study

Business Intelligence (BI) projects are long and painful endeavors that employ a variety of design methodologies, inspired mostly by software engineering and project management lifecycle models. In recent BI research, new design methodologies are emerging founded on conceptual business models that capture business objectives, strategies, and more. Their claim is that they facilitate the description of the problem-at-hand, its analysis towards a solution, and the implementation of that solution. The key question explored in this work is:Are such models actually useful to BI design practitioners? To answer this question, we conducted an in situ empirical evaluation based on an on-going BI project for a Toronto hospital. The lessons learned from the study include: confirmation that the BI implementation is well-supported by models founded on business concepts; evidence that these models enhance communication within the project team and business stakeholders; and, evidence that there is a need for business modeling to capture BI requirements and, from those, derive and implement BI designs.

Making Data Meaningful: The Business Intelligence Model and Its Formal Semantics in Description Logics

Business Intelligence (BI) offers great opportunities for strategic analysis of current and future business operations; however, existing BI tools typically provide data-oriented responses to queries, difficult to understand in terms of business objectives and strategies. To make BI data meaningful, we need a conceptual modeling language whose primitive concepts represent business objectives, processes, opportunities and threats. We have previously introduced such a language, the Business Intelligence Model (BIM). In this paper we consolidate and rationalize earlier work on BIM, giving a precise syntax, reducing the number of fundamental concepts by using meta-attributes, and introducing the novel notion of “pursuit”. Significantly, we also provide a formal semantics of BIM using a subset of the OWL Description Logic (DL). Using this semantics as a translation, DL reasoners can be exploited to (1) propagate evidence and goal pursuit in support of “what if?” reasoning, (2) allow extensions to the BIM language, (3) detect inconsistencies in specific BIM models, and (4) automatically classify defined concepts relative to existing concepts, organizing the model.

Enterprise Modeling for Business Intelligence

Business Intelligence (BI) software aims to enable business users to easily access and analyze relevant enterprise information so that they can make timely and fact-based decisions. However, despite user-friendly features such as dashboards and other visualizations, business users still find BI software hard to use and inflexible for their needs. Furthermore, current BI initiatives require significant efforts by IT specialists to understand business operations and requirements, in order to build BI applications and help formulate queries. In this paper, we present a vision for BI that is driven by enterprise modeling. The Business Intelligence Model (BIM) aims to enable business users to conceptualize business operations and strategies and performance indicators in a way that can be connected to enterprise data through highly automated tools. The BIM draws upon well-established business practices such as Balanced Scorecard and Strategy Maps as well as requirements and conceptual modeling techniques such as goal modeling. The connection from BIM to databases is supported by a complementary research effort on conceptual data integration.

Dependency discovery in data quality

A conceptual framework for the automatic discovery of dependencies between data quality dimensions is described. Dependency discovery consists in recovering the dependency structure for a set of data quality dimensions measured on attributes of a database. This task is accomplished through the data mining methodology, by learning a Bayesian Network from a database. The Bayesian Network is used to analyze dependency between data quality dimensions associated with different attributes. The proposed framework is instantiated on a real world database. The task of dependency discovery is presented in the case when the following data quality dimensions are considered; accuracy, completeness, and consistency. The Bayesian Network model shows how data quality can be improved while satisfying budget constraints.

Towards Model-Based Support for Managing Organizational Transformation

In an increasingly connected and dynamic world, most organizations are continuously evolving their business objectives, processes and operations through ongoing transformation and renewal, while their external environment is changing simultaneously. In such a setting, it is imperative for organizations to continuously monitor their performance and adjust when there is a need. The technology that delivers this monitoring capability is called Business Intelligence (BI), and over the years it has come to play a central role in business operations and governance. Unfortunately, there is a huge cognitive gap between the strategic business level view of goals, processes, and performance on one hand, and the technological/implementation view of databases, networks, and computational processing offered by BI tools on the other.

Towards a taxonomy of syntactic and semantic matching mechanisms for aspect-oriented modeling

Aspect-oriented modeling (AOM) techniques have become increasingly popular over the last decade, as they enable improved modularity, separation of concerns, and reusability over conventional requirements and design modeling techniques. However, AOM notations typically employ pointcut matching mechanisms based solely on syntactic elements. In order to make aspects more generic and more robust to changes and to different modeling styles, semantic matching must be better exploited. We present a taxonomy that aims to classify matching mechanisms based on how syntactic or semantic information is used during the matching process, thus defining levels of sophistication for matching mechanisms from simple syntactic approaches to complex semantic approaches. We are particularly investigating how schema matching techniques developed in the database research community are applicable in this context. We illustrate the feasibility and potential benefits through examples based on the Aspect-oriented User Requirements Notation (AoURN).