Proceedings. International Conference on Data Engineering最新文献

The Wearables for Health Toolkit (W4H Toolkit) is an open-source platform that provides a robust, end-to-end solution for the centralized management and analysis of wearable data. With integrated tools and frameworks, the toolkit facilitates seamless data acquisition, integration, storage, analysis, and visualization of both stored and streaming data from various wearable devices. The W4H Toolkit is designed to provide medical researchers and health practitioners with a unified framework that enables the analysis of health-related data for various clinical applications. We provide an overview of the system and demonstrate how it can be used by health researchers to import and analyze a wide range of wearable data and perform data analysis, highlighting the versatility and functionality of the system across diverse healthcare domains and applications.

The study of patients in Intensive Care Units (ICUs) is a crucial task in critical care research which has significant implications both in identifying clinical risk factors and defining institutional guidances. The mortality study of ICU patients is of particular interest because it provides useful indications to healthcare institutions for improving patients experience, internal policies, and procedures (e.g. allocation of resources). To this end, many research works have been focused on the length of stay (LOS) for ICU patients as a feature for studying the mortality. In this work, we propose a novel mortality study based on the notion of burstiness, where the temporal information of patients longitudinal data is taken into consideration. The burstiness of temporal data is a popular measure in network analysis and time-series anomaly detection, where high values of burstiness indicate presence of rapidly occurring events in short time periods (i.e. burst). Our intuition is that these bursts may relate to possible complications in the patient's medical condition and hence provide indications on the mortality. Compared to the LOS, the burstiness parameter captures the temporality of the medical events providing information about the overall dynamic of the patients condition. To the best of our knowledge, we are the first to apply the burstiness measure in the clinical research domain. Our preliminary results on a real dataset show that patients with high values of burstiness tend to have higher mortality rate compared to patients with more regular medical events. Overall, our study shows promising results and provides useful insights for developing predictive models on temporal data and advancing modern critical care medicine.

According to the Centers for Disease Control, in the United States there are 6.8 million children living with asthma. Despite the importance of the disease, the available prognostic tools are not sufficient for biomedical researchers to thoroughly investigate the potential risks of the disease at scale. To overcome these challenges we present a big data integration and analysis infrastructure developed by our Data and Software Coordination and Integration Center (DSCIC) of the NIBIB-funded Pediatric Research using Integrated Sensor Monitoring Systems (PRISMS) program. Our goal is to help biomedical researchers to efficiently predict and prevent asthma attacks. The PRISMS-DSCIC is responsible for collecting, integrating, storing, and analyzing real-time environmental, physiological and behavioral data obtained from heterogeneous sensor and traditional data sources. Our architecture is based on the Apache Kafka, Spark and Hadoop frameworks and PostgreSQL DBMS. A main contribution of this work is extending the Spark framework with a mediation layer, based on logical schema mappings and query rewriting, to facilitate data analysis over a consistent harmonized schema. The system provides both batch and stream analytic capabilities over the massive data generated by wearable and fixed sensors.

The exponential growth of high dimensional biological data has led to a rapid increase in demand for automated approaches for knowledge production. Existing methods rely on two general approaches to address this challenge: 1) the Theory-driven approach, which utilizes prior accumulated knowledge, and 2) the Data-driven approach, which solely utilizes the data to deduce scientific knowledge. Both of these approaches alone suffer from bias toward past/present knowledge, as they fail to incorporate all of the current knowledge that is available to make new discoveries. In this paper, we show how an integrated method can effectively address the high dimensionality of big biological data, which is a major problem for pure data-driven analysis approaches. We realize our approach in a novel two-step analytical workflow that incorporates a new feature selection paradigm as the first step to handling high-throughput gene expression data analysis and that utilizes graphical causal modeling as the second step to handle the automatic extraction of causal relationships. Our results, on real-world clinical datasets from The Cancer Genome Atlas (TCGA), demonstrate that our method is capable of intelligently selecting genes for learning effective causal networks.

Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuous generated data in the real world tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations in the context of continuous data release. First, we model the temporal correlations using Markov model and analyze the privacy leakage of a DP mechanism when adversaries have knowledge of such temporal correlations. Our analysis reveals that the privacy loss of a DP mechanism may accumulate and increase over time. We call it temporal privacy leakage. Second, to measure such privacy loss, we design an efficient algorithm for calculating it in polynomial time. Although the temporal privacy leakage may increase over time, we also show that its supremum may exist in some cases. Third, to bound the privacy loss, we propose mechanisms that convert any existing DP mechanism into one against temporal privacy leakage. Experiments with synthetic data confirm that our approach is efficient and effective.

The problem of simultaneously clustering columns and rows (co-clustering) arises in important applications, such as text data mining, microarray analysis, and recommendation system analysis. Compared with the classical clustering algorithms, co-clustering algorithms have been shown to be more effective in discovering hidden clustering structures in the data matrix. The complexity of previous co-clustering algorithms is usually O(m × n), where m and n are the numbers of rows and columns in the data matrix respectively. This limits their applicability to data matrices involving a large number of columns and rows. Moreover, some huge datasets can not be entirely held in main memory during co-clustering which violates the assumption made by the previous algorithms. In this paper, we propose a general framework for fast co-clustering large datasets, CRD. By utilizing recently developed sampling-based matrix decomposition methods, CRD achieves an execution time linear in m and n. Also, CRD does not require the whole data matrix be in the main memory. We conducted extensive experiments on both real and synthetic data. Compared with previous co-clustering algorithms, CRD achieves competitive accuracy but with much less computational cost.

Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selection, feature transformation (or feature projection) and projected clustering. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In many emerging biomedical applications, however, scientists are interested in the local latent patterns held by feature subsets, which may be invisible via any global transformation. In this paper, we investigate the problem of finding local linear correlations in high dimensional data. Our goal is to find the latent pattern structures that may exist only in some subspaces. We formalize this problem as finding strongly correlated feature subsets which are supported by a large portion of the data points. Due to the combinatorial nature of the problem and lack of monotonicity of the correlation measurement, it is prohibitively expensive to exhaustively explore the whole search space. In our algorithm, CARE, we utilize spectrum properties and effective heuristic to prune the search space. Extensive experimental results show that our approach is effective in finding local linear correlations that may not be identified by existing methods.