Jens H. Weber

Understanding database schema evolution

Database reverse engineering (DRE) has traditionally been carried out by considering three main information sources: (1) the database schema, (2) the stored data, and (3) the application programs. Not all of these information sources are always available, or of sufficient quality to inform the DRE process. For example, getting access to real-world data is often extremely problematic for information systems that maintain private data. In recent years, the analysis of the evolution history of software programs have gained an increasing role in reverse engineering in general, but comparatively little such research has been carried out in the context of database reverse engineering. The goal of this paper is to contribute to narrowing this gap and exploring the use of the database evolution history as an additional information source to aid database schema reverse engineering. We present a tool-supported method for analyzing the evolution history of legacy databases, and we report on a large-scale case study of reverse engineering a complex information system and curate it as a benchmark for future research efforts within the community. We present a tool-supported method to analyze the history of a database schema.The method makes use of mining software repositories (MSR) techniques.We report on the application of the method to a large-scale case study.

Understanding Schema Evolution as a Basis for Database Reengineering

Software repositories can provide valuable information for facilitating software reengineering efforts. In recent years, many researchers have started to follow a holistic approach, considering diverse software artifacts and the links existing between them. However, when analyzing data-intensive systems, comparatively little attention has been devoted to the analysis of an important system artifact: the database. Even fewer approaches attempt to uncover facts about the evolution history of database schemas. We have developed a tool-supported method for analyzing and visualizing database schema history. This paper reports early results of applying and validating this method. We discuss our experiences to date and point out several novel research perspectives in this domain.

SCOOP - The Social Collaboratory for Outcome Oriented Primary Care

Many primary care clinics have transitioned from paper-based record keeping to computer-based Electronic Medical Record (EMR) systems. This transition provides opportunities for computer-based data analytics in support of practice improvement and more evidence-based clinical research. Unfortunately, the data in primary care EMRs is often not readily accessible to researchers, who often have to overcome significant political, organizational and technical hurdles before gaining access to such data. As a consequence, knowledge discovery and translation has been slow and burdensome in this area. Primary care research networks (PCRN) have been proposed as a way to addressing these limitations. This paper reports on the development of a PCRN in British Columbia, referred to as SCOOP (The Social Collaboratory for Outcome Oriented Primary Care). We describe its technical architecture and draw comparisons to related and previous initiatives.

Introducing GlucoFit: An Assistive Technology for Monitoring and Managing Diabetes

This paper introduces GlucoFit, an application designed for diabetics to help them manage and monitor their blood sugar (glucose) level. It allows users to input their blood glucose readings and insulin injections. With the use of the exercise information from Fit Bit, an exercise monitoring device, GlucoFit also provides suggestions for exercise goals to increase the users physical activity. The GlucoFit server and web client were developed using the Django web framework and Python. The application Tasty pie was used to create the GlucoFit REST API. In addition to the web client, a mobile application was developed for the iPhone, allowing users to record and review blood glucose and insulin injection data and monitor fitness goals.

Emerging IT Trends in Healthcare and Well-Being

The importance of IT in healthcare continues to grow, driven by disruptive changes, including financial pressure, an aging population, the spread of connectivity and mobile technology, and significant advances in the biology of disease and the practice of medicine. These changes demand new advances in health IT. This special issue discusses the most recent trends in IT for healthcare and well-being.

Uncovering Variability Models for Software Ecosystems from Multi-Repository Structures

Variability is a significant source of complexity in many large-scale software systems. Software variability must be managed in order to effectively tame the arising complexity. Consequently, variability management processes are at the heart of current software product line engineering practices. However, legacy software systems exist that have not been developed with such practices. Moreover, an increasing amount of software is developed in large, fragmented communities, also referred to as software ecosystems. Variability in such systems is often not explicitly managed and causes significant difficulties during software maintenance and evolution. Methods and tools for uncovering and explicitly managing this variability have been subject to ongoing research. This paper presents our research in progress of empirically studying the application and combination of such methods in the context of real-world industrial case study in the health care domain.

Big Data in Healthcare - Defining the Digital Persona through User Contexts from the Micro to the Macro. Contribution of the IMIA Organizational and Social Issues WG.

OBJECTIVES While big data offers enormous potential for improving healthcare delivery, many of the existing claims concerning big data in healthcare are based on anecdotal reports and theoretical vision papers, rather than scientific evidence based on empirical research. Historically, the implementation of health information technology has resulted in unintended consequences at the individual, organizational and social levels, but these unintended consequences of collecting data have remained unaddressed in the literature on big data. The objective of this paper is to provide insights into big data from the perspective of people, social and organizational considerations. METHOD We draw upon the concept of persona to define the digital persona as the intersection of data, tasks and context for different user groups. We then describe how the digital persona can serve as a framework to understanding sociotechnical considerations of big data implementation. We then discuss the digital persona in the context of micro, meso and macro user groups across the 3 Vs of big data. RESULTS We provide insights into the potential benefits and challenges of applying big data approaches to healthcare as well as how to position these approaches to achieve health system objectives such as patient safety or patient-engaged care delivery. We also provide a framework for defining the digital persona at a micro, meso and macro level to help understand the user contexts of big data solutions. CONCLUSION While big data provides great potential for improving healthcare delivery, it is essential that we consider the individual, social and organizational contexts of data use when implementing big data solutions.

Differential Privacy in Practice

Differential privacy (DP) has attracted considerable attention as the method of choice for releasing aggregate query results making it hard to infer information about individual records in the database. The most common way to achieve DP is to add noise following Laplace distribution. In this paper, we study differential privacy from a utility point of view for single and multiple queries. We examine the relationship between the cumulative probability of noise and the privacy degree. Using this analysis and the notion of relative error, we show when for a given problem it is reasonable to employ a differentially private algorithm without losing a certain level of utility. For the case of multiple queries, we introduce a simple DP method called Differential (DIFF) that adds noise proportional to a query index used to express our preferences for having different noise scales for different queries. We also introduce an equation capturing when DIFF satisfies a user-given relative error threshold.

Certification of Open Source Software – A Scoping Review

Open source software (OSS) systems are being used for increasingly critical functions in modern societies, e.g., in health care, finance, government, defense, and other safety and security sensitive sectors. There is an increasing interest in software certification as a means to assure quality and dependability of such systems. However, the development processes and organizational structures of OSS projects can be substantially different from traditional closed-source projects. The distributed, “bazaar-style” approach to software development in OSS systems is often perceived incompatible with certification. This paper presents the results of a scoping review on certification in OSS systems in order to identify and categorize key issues and provide a comprehensive overview of the current evidence on this topic.

Closing the gap: enacting knowledge transfer between engineering and use of healthcare software

Healthcare is an information and knowledge intensive industry where software systems are playing increasingly important roles. Thus the risks of harm are increasing. Previously, we have proposed a knowledge translation (KT) model to help bridge the gap between software engineering and clinicians. This paper highlights the application of our proposed knowledge translation loop. We have enacted it through a series of two studies: 1. A randomized control trial to evaluate a software-based decision support function in an electronic medical record (EMR) and 2. A lead user based user interface and safety design study to create an improved design for an EMR medication module. We also provide lessons learned and future directions on integrating our KT model in regular software quality management systems.