Philipp Baumgärtel

Efficient and cost-aware operator placement in heterogeneous stream-processing environments

Operator placement for distributed stream-processing systems is still a challenging problem that can be modeled as a Task Assignment Problem (TAP). Multiple objectives are relevant for the optimization in heterogeneous stream-processing systems as there are different capabilities of the underlying networks and stream-processing nodes. We present an approach based on linear programming relaxation and iterative deterministic rounding. It uses an efficient linearization approach for the quadratic objective function that results from the TAP.

Health Economic Impact of a Pulmonary Artery Pressure Sensor for Heart Failure Telemonitoring: A Dynamic Simulation.

AIMS Recently, a permanently implantable wireless system, designed to monitor and manage pulmonary artery (PA) pressures remotely, demonstrated significant reductions in heart failure (HF) hospitalizations in high-risk symptomatic patients, regardless of ejection fraction. The objectives of this study were to simulate the estimated clinical and economic impact in Germany of generalized use of this PA pressure monitoring system considering reductions of HF hospitalizations and the improvement in Quality of Life. MATERIALS AND METHODS Based on the Prospective Health Technology Assessment approach, we simulated the potential of the widespread application of PA pressure monitoring on the German healthcare system for the period 2009-2021. RESULTS This healthcare economic simulation formulated input assumptions based on results from the CHAMPION Trial, a multicenter, prospective, randomized controlled U.S. trial that demonstrated a 37% reduction of hospitalizations in persistently symptomatic previous HF patients. Based on these results, an estimated 114,800 hospitalizations would expected to be avoided. This effect would potentially save an estimated €522 million, an equivalent of

Black-box determination of cost models' parameters for federated stream-processing systems

575 million, during the entire simulation period. CONCLUSION This healthcare economic modeling of the PA pressure monitoring systems impact demonstrates substantial clinical and economic benefits in the German healthcare system.

ForCE: Is Estimation of Data Completeness Through Time Series Forecasts Feasible?

For distribution and deployment of queries in distributed stream-processing environments, it is vital to estimate the expected costs in advance. Having heterogeneous Stream-Processing Systems (SPSs) running on various hosts, the parameters of a cost model for an operator must be determined by measurements for each relevant combination of an SPS and hardware. This paper presents a black-box method that determines the parameters of appropriate cost models that regard system-specific behavior. For some SPSs, there might not be any appropriate cost model available due to the lack of internal knowledge. If no cost model is available for any reason, we provide and apply a non-parametric model.

A Benchmark for Multidimensional Statistical Data

Measuring the completeness of a data population often requires either expert knowledge or the presence of reference data. If neither is available, measuring population completeness becomes nontrivial. We present the ForCE approach (Forecasting for Completeness Estimation), a method to estimate the completeness of timestamped data using time series forecasting. We evaluate the method’s feasibility using a medical domain real-world dataset, which we provide for download. The method is compared to three baselines. ForCE manages to surpass all three.

Toward Pay-As-You-Go Data Integration for Healthcare Simulations

ProHTA Prospective Health Technology Assessment is a simulation project that aims at estimating the outcome of new medical innovations at an early stage. To this end, hybrid and modular simulations are employed. For this large scale simulation project, efficient management of multidimensional statistical data is important. Therefore, we propose a benchmark to evaluate query processing of this kind of data in relational and non-relational databases. We compare our benchmark with existing approaches and point out differences. This paper presents a mapping to a flexible relational model, JSON documents and RDF. The queries defined for our benchmark are mapped to SQL, SPARQL, the MongoDB query language and MapReduce. Using our benchmark, we evaluate these different systems and discuss differences between them.

A Query Language for Workflow Instance Data

ProHTA (Prospective Health Technology Assessment) aims at understanding the impact of innovative medical processes and technologies at an early stage. To that end, large scale healthcare simulations are employed to estimate the effects of potential innovations. Simulation techniques are also utilized to detect areas with a high potential for improving the supply chain of healthcare. The data needed for both validating and adjusting these simulations typically comes from various heterogeneous sources and is often preaggregated and insufficiently documented. Thus, new data management techniques are required to cope with these conditions. Because of the high initial integration effort, we propose a pay-as-you-go approach using RDF. Thereby, data storage is separated from semantic annotation. Our proposed system offers automatic initial integration of various data sources. Additionally, it provides methods for searching semantically annotated data and for loading it into the simulation. The user can add annotations to the data in order to enable semantic integration on demand. In this paper, we demonstrate the feasibility of this approach with a prototype implementation. We discuss benefits and remaining challenges.

PAY-AS-YOU-GO DATA QUALITY IMPROVEMENT FOR MEDICAL CENTERS

In our simulation project ProHTA (Prospective Health Technology Assessment), we want to estimate the outcome of new medical innovations. To this end, we employ agent-based simulations that require workflow definitions with associated data about workflow instances. For example, to optimize the clinical pathways of patients with stroke we need the time and associated costs of each step in the clinical pathway. We adapt an existing conceptual model to store workflow definitions and instance data in RDF. This paper presents a query language to aggregate and query workflow instance data. That way, we support domain experts in analyzing simulation input and output. We present a heuristic algorithm for efficient query processing. Finally, we evaluate the performance of our query processing algorithm and compare it to SPARQL.