Orchestration and Management of Adaptive IoT-centric Distributed Applications.

Current Internet of Things (IoT) devices provide a diverse range of functionalities, ranging from measurement and dissemination of sensory data observation, to computation services for real-time data stream processing. In extreme situations such as emergencies, a significant benefit of IoT devices is that they can help gain a more complete situational understanding of the environment. However, this requires the ability to utilize IoT resources while taking into account location, battery life, and other constraints of the underlying edge and IoT devices. A dynamic approach is proposed for orchestration and management of distributed workflow applications using services available in cloud data centers, deployed on servers, or IoT devices at the network edge. Our proposed approach is specifically designed for knowledge-driven business process workflows that are adaptive, interactive, evolvable and emergent. A comprehensive empirical evaluation shows that the proposed approach is effective and resilient to situational changes.

Collaborative Business Process Fault Resolution in the Services Cloud.

The emergence of cloud and edge computing has enabled rapid development and deployment of Internet-centric distributed applications. There are many platforms and tools that can facilitate users to develop distributed business process (BP) applications by composing relevant service components in a plug and play manner. However, there is no guarantee that a BP application developed in this way is fault-free. In this paper, we formalize the problem of collaborative BP fault resolution which aims to utilize information from existing fault-free BPs that use similar services to resolve faults in a user developed BP. We present an approach based on association analysis of pairwise transformations between a faulty BP and existing BPs to identify the smallest possible set of transformations to resolve the fault(s) in the user developed BP. An extensive experimental evaluation over both synthetically generated faulty BPs and real BPs developed by users shows the effectiveness of our approach.

An Integrated Framework for Fault Resolution in Business Processes.

Cloud and edge-computing based platforms have enabled rapid development of distributed business process (BP) applications in a plug and play manner. However, these platforms do not provide the needed capabilities for identifying or repairing faults in BPs. Faults in BP may occur due to errors made by BP designers because of their lack of understanding of the underlying component services, misconfiguration of these services, or incorrect/incomplete BP workflow specifications. Such faults may not be discovered at design or development stage and may occur at runtime. In this paper, we present a unified framework for automated fault resolution in BPs. The proposed framework employs a novel and efficient fault resolution approach that extends the generate-and-validate program repair approach. In addition, we propose a hybrid approach that performs fault resolution by analyzing a faulty BP in isolation as well as by comparing with other BPs using similar services. This hybrid approach results in improved accuracy and broader coverage of fault types. We also perform an extensive experimental evaluation to compare the effectiveness of the proposed approach using a dataset of 208 faulty BPs.

BP-DEBUG: A Fault Debugging and Resolution Tool for Business Processes.

Cloud computing and Internet-ware software paradigm have enabled rapid development of distributed business process (BP) applications. Several tools are available to facilitate automated/ semi-automated development and deployment of such distributed BPs by orchestrating relevant service components in a plug-and-play fashion. However, the BPs developed using such tools are not guaranteed to be fault-free. In this demonstration, we present a tool called BP-DEBUG for debugging and automated repair of faulty BPs. BP-DEBUG implements our Collaborative Fault Resolution (CFR) approach that utilizes the knowledge of existing BPs with a similar set of web services fault detection and resolution in a given user BP. Essentially, CFR attempts to determine any semantic and structural differences between a faulty BP and related BPs and computes a minimum set of transformations which can be used to repair the faulty BP. Demo url: https://youtu.be/mf49oSekLOA.

A Framework for Dynamic Composition and Management of Emergency Response Processes.

An emergency response process outlines the workflow of different activities that need to be performed in response to an emergency. Effective emergency response requires communication and coordination with the operational systems belonging to different collaborating organizations. Therefore, it is necessary to establish information sharing and system-level interoperability among the diverse operational systems. Unlike typical e-government processes that are well structured and have a well-defined outcome, emergency response processes are knowledge-centric and their workflow structure and execution may evolve as the incident unfolds. It is impractical to define static plans and response process workflows for every possible situation. Instead, a dynamic response should be adaptable to the changing situation. We present an integrated approach that facilitates the dynamic composition of an executable response process. The proposed approach employs ontology-based reasoning to determine the default actions and resource requirements for the given incident and to identify relevant response organizations based on their jurisdictional and mutual aid agreement rules. The Web service APIs of the identified response organizations are then used to generate an executable response process that evolves dynamically. The proposed approach is implemented and experimentally validated using an example scenario derived from the FEMA Hazardous Materials Tabletop Exercises Manual.

A Service-oriented Framework for Developing Personalized Patient Care Plans for COVID-19.

The COVID-19 pandemic has identified weaknesses and stresses in the existing healthcare and governance system, even in the most developed countries. Given the scale and scope of the pandemic, existing healthcare systems are heavily resource constrained, and home-based isolation has been considered as a potential first step for reducing both the disease spread and the stress on the healthcare system. However, the needs and requirements of home-based isolation are extremely unique for each patient, depending on their medical condition and comorbidities, family responsibilities, and environmental constraints. Therefore, it is necessary to develop personalized patient care plans to ensure that the needs of each patient are appropriately met. In this paper we propose a service oriented framework that allows dynamic composition and management of such plans assuming existence of an appropriate knowledge base and availability of web-services interfaces of the underlying systems of caregivers and service providers. We develop a prototype implementation to show the feasibility of the proposed framework and discuss the challenges/issues in deploying such a system in practice.

Greater Survival Improvement in African American vs. Caucasian Women with Hormone Negative Breast Cancer.

Background: African American women have not benefited equally from recently improved breast cancer survival. We investigated if this was true for all subsets. Methods: We identified 395,170 patients with breast adenocarcinoma from the SEER database from 1990 to 2011 with designated race, age, stage, grade, ER and PR status, marital status and laterality, as control. We grouped patients into two time periods, 1990-2000 and 2001-2011, three age categories, under 40, 40-69 and ≥ 70 years and two stage categories, I-III and IV. We used the Kaplan-Meier and logrank tests to compare survival curves. We stratified data by patient- and tumor-associated variables to determine co-variation among confounding factors using the Pearson Chi-square test and Cox proportional hazards regression to determine hazard ratios (HR) to compare survival. Results: Stage I-III patients of both races ≥ 70 years old, African American widowed patients and Caucasians with ER- and PR- tumors had worse improvements in survival in 2001-2011 than younger, married or hormone receptor positive patients, respectively. In contrast, African Americans with ER- (Cox HR 0.70 [95% CI 0.65-0.76]) and PR- (Cox HR 0.67 [95% CI 0.62-0.72]) had greater improvement in survival in 2001-2011 than Caucasians with ER- (Cox HR 0.81 [95% CI 0.78-0.84]) and PR- disease (Cox HR 0.75 [95% CI 0.73-0.78]). This was not associated with changes in distribution of tumor or patient attributes. Conclusions: African American women with stage I-III ER- and PR- breast cancer had greater improvement in survival than Caucasians in 2001-2011. This is the first report of an improvement in racial disparities in survival from breast cancer in a subset of patients.

Blockchain Based Auditable Access Control for Distributed Business Processes.

The use of blockchain technology has been proposed to provide auditable access control for individual resources. However, when all resources are owned by a single organization, such expensive solutions may not be needed. In this work we focus on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains can provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may be overlapping in terms of the component conditions/rules, and simply using existing solutions would result in repeated evaluation of user's authorization separately for each resource, leading to significant overhead in terms of cost and computation time over the blockchain. To address this challenge, we propose an approach that formulates a constraint optimization problem to generate an optimal composite access control policy. This policy is in compliance with all the local access control policies and minimizes the policy evaluation cost over the blockchain. The developed smart contract(s) can then be deployed to the blockchain, and used for access control enforcement. We also discuss how the access control enforcement can be audited using a game-theoretic approach to minimize cost. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and experimentally validated the effectiveness and efficiency of our approach.

DIFFERENTIALLY PRIVATE OUTLIER DETECTION IN A COLLABORATIVE ENVIRONMENT.

Outlier detection is one of the most important data analytics tasks and is used in numerous applications and domains. The goal of outlier detection is to find abnormal entities that are significantly different from the remaining data. Often the underlying data is distributed across different organizations. If outlier detection is done locally, the results obtained are not as accurate as when outlier detection is done collaboratively over the combined data. However, the data cannot be easily integrated into a single database due to privacy and legal concerns. In this paper, we address precisely this problem. We first define privacy in the context of collaborative outlier detection. We then develop a novel method to find outliers from both horizontally partitioned and vertically partitioned categorical data in a privacy-preserving manner. Our method is based on a scalable outlier detection technique that uses attribute value frequencies. We provide an end-to-end privacy guarantee by using the differential privacy model and secure multiparty computation techniques. Experiments on real data show that our proposed technique is both effective and efficient.

Secure and Efficient k-NN Queries.

Given the morass of available data, ranking and best match queries are often used to find records of interest. As such, k-NN queries, which give the k closest matches to a query point, are of particular interest, and have many applications. We study this problem in the context of the financial sector, wherein an investment portfolio database is queried for matching portfolios. Given the sensitivity of the information involved, our key contribution is to develop a secure k-NN computation protocol that can enable the computation k-NN queries in a distributed multi-party environment while taking domain semantics into account. The experimental results show that the proposed protocols are extremely efficient.

A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis.

Big data coupled with precision medicine has the potential to significantly improve our understanding and treatment of complex disorders, such as cancer, diabetes, depression, etc. However, the essential problem is that data are stuck in silos, and it is difficult to precisely identify which data would be relevant and useful for any particular type of analysis. While the process to acquire and access biomedical data requires significant effort, in many cases the data may not provide much insight to the problem at hand. Therefore, there is a need to be able to measure the utility/relevance of additional datasets for a particular biomedical research task without direct access to the data. Towards this, in this paper, we develop a privacy-preserving approach to create synthetic data that can provide a firstorder approximation of utility. We evaluate the proposed approach with several biomedical datasets in the context of regression and classification tasks and discuss how it can be incorporated into existing data management systems such as REDCap.

African American Race is an Independent Risk Factor in Survival from Initially Diagnosed Localized Breast Cancer.

BACKGROUND: African American race negatively impacts survival from localized breast cancer but co-variable factors confound the impact. METHODS: Data sets were analyzed from the Surveillance, Epidemiology and End Results (SEER) directories from 1973 to 2011 consisting of patients with designated diagnosis of breast adenocarcinoma, race as White or Caucasian, Black or African American, Asian, American Indian or Alaskan Native, Native Hawaiian or Pacific Islander, age, stage I, II or III, grade 1, 2 or 3, estrogen receptor or progesterone receptor positive or negative, marital status as single, married, separated, divorced or widowed and laterality as right or left. The Cox Proportional Hazards Regression model was used to determine hazard ratios for survival. Chi square test was applied to determine the interdependence of variables found significant in the multivariable Cox Proportional Hazards Regression analysis. Cells with stratified data of patients with identical characteristics except African American or Caucasian race were compared. RESULTS: Age, stage, grade, ER and PR status and marital status significantly co-varied with race and with each other. Stratifications by single co-variables demonstrated worse hazard ratios for survival for African Americans. Stratification by three and four co-variables demonstrated worse hazard ratios for survival for African Americans in most subgroupings with sufficient numbers of values. Differences in some subgroupings containing poor prognostic co-variables did not reach significance, suggesting that race effects may be partly overcome by additional poor prognostic indicators. CONCLUSIONS: African American race is a poor prognostic indicator for survival from breast cancer independent of 6 associated co-variables with prognostic significance.

Identifying inference attacks against healthcare data repositories.

Health care data repositories play an important role in driving progress in medical research. Finding new pathways to discovery requires having adequate data and relevant analysis. However, it is critical to ensure the privacy and security of the stored data. In this paper, we identify a dangerous inference attack against naive suppression based approaches that are used to protect sensitive information. We base our attack on the querying system provided by the Healthcare Cost and Utilization Project, though it applies in general to any medical database providing a query capability. We also discuss potential solutions to this problem.

Privacy preserving integration of health care data.

For health care related research studies the medical records of patients may need to be retrieved from multiple sites with different regulations on the disclosure of health information. Given the sensitive nature of health care information, privacy is a major concern when patients' health care data is used for research purposes. In this paper, we propose an approach for integration and querying of health care data from multiple sources in a secure and privacy preserving manner.