Rapid Development of Data Generators Using Meta Generators in PDGF

ABSTRACT Generating data sets for the performance testing of database systems on a particular hardware configuration and application domain is a very time consuming and tedious process. It is time consuming, because of the large amount of data that needs to be generated and tedious, because new data generators might need to be developed or existing once adjusted. The difficulty in generating this data is amplified by constant advances in hardware and software that allow the testing of ever larger and more complicated systems. In this paper, we present an approach for rapidly developing customized data generators. Our approach, which is based on the Parallel Data Generator Framework (PDGF), deploys a new concept of so called meta generators. Meta generators extend the concept of column-based generators in PDGF. Deploying meta generators in PDGF significantly reduces the development effort of customized data generators, it facilitates their debugging and eases their maintenance

RODI: Benchmarking Relational-to-Ontology Mapping Generation Quality

Accessing and utilizing enterprise or Web data that is scattered across multiple data sources is an important task for both applications and users. Ontology-based data integration, where an ontology mediates between the raw data and its consumers, is a promising approach to facilitate such scenarios. This approach crucially relies on useful mappings to relate the ontology and the data, the latter being typically stored in relational databases. A number of systems to support the construction of such mappings have recently been developed. A generic and effective benchmark for reliable and comparable evaluation of the practical utility of such systems would make an important contribution to the development of ontology-based data integration systems and their application in practice. We have proposed such a benchmark, called RODI. In this paper, we present a new version of RODI, which significantly extends our previous benchmark, and we evaluate various systems with it. RODI includes test scenarios from the domains of scientific conferences, geographical data, and oil and gas exploration. Scenarios are constituted of databases, ontologies, and queries to test expected results. Systems that compute relational-to-ontology mappings can be evaluated using RODI by checking how well they can handle various features of relational schemas and ontologies, and how well the computed mappings work for query answering. Using RODI, we conducted a comprehensive evaluation of seven systems

Risk Stratification for Patients in Cardiogenic Shock After Acute Myocardial Infarction

BACKGROUND Mortality in cardiogenic shock (CS) remains high. Early risk stratification is crucial to make adequate treatment decisions. OBJECTIVES This study sought to develop an easy-to-use, readily available risk prediction score for short-term mortality in patients with CS, derived from the IABP-SHOCK II (Intraaortic Balloon Pump in Cardiogenic Shock) trial. METHODS The score was developed using a stepwise multivariable regression analysis. RESULTS Six variables emerged as independent predictors for 30-day mortality and were used as score parameters: age > 73 years, prior stroke, glucose at admission >10.6 mmol/l (191 mg/dl), creatinine at admission >132.6 mmol/l (1.5 mg/dl), Thrombolysis In Myocardial Infarction flow grade 5 mmol/l. Either 1 or 2 points were attributed to each variable, leading to a score in 3 risk categories: low (0 to 2), intermediate (3 or 4), and high (5 to 9). The observed 30-day mortality rates were 23.8%, 49.2%, and 76.6%, respectively (p <0.0001). Validation in the IABP-SHOCK II registry population showed good discrimination with an area under the curve of 0.79. External validation in the CardShock trial population (n = 137) showed short-term mortality rates of 28.0% (score 0 to 2), 42.9% (score 3 to 4), and 77.3% (score 5 to 9; p <0.001) and an area under the curve of 0.73. Kaplan-Meier analysis revealed a stepwise increase in mortality between the different score categories (0 to 2 vs. 3 to 4: p = 0.04; 0 to 2 vs. 5 to 9: p = 0.008). CONCLUSIONS The IABP-SHOCK II risk score can be easily calculated in daily clinical practice and strongly correlated with mortality in patients with infarct-related CS. It may help stratify patient risk for short-term mortality and might, thus, facilitate clinical decision making. (Intraaortic Balloon Pump in Cardiogenic Shock II [ IABP-SHOCK II]; NCT00491036) (J Am Coll Cardiol 2017; 69: 1913-20) (C) 2017 by the American College of Cardiology Foundation.Peer reviewe

Prognostic Impact of Active Mechanical Circulatory Support in Cardiogenic Shock Complicating Acute Myocardial Infarction, Results from the Culprit-Shock Trial

Objectives: To analyze the use and prognostic impact of active mechanical circulatory support (MCS) devices in a large prospective contemporary cohort of patients with cardiogenic shock (CS) complicating acute myocardial infarction (AMI). Background: Although increasingly used in clinical practice, data on the efficacy and safety of active MCS devices in patients with CS complicating AMI are limited. Methods: This is a predefined subanalysis of the CULPRIT-SHOCK randomized trial and prospective registry. Patients with CS, AMI and multivessel coronary artery disease were categorized in two groups: (1) use of at least one active MCS device vs. (2) no active MCS or use of intra-aortic balloon pump (IABP) only. The primary endpoint was a composite of all-cause death or renal replacement therapy at 30 days. Results: Two hundred of 1055 (19%) patients received at least one active MCS device (n = 112 Impella®; n = 95 extracorporeal membrane oxygenation (ECMO); n = 6 other devices). The primary endpoint occurred significantly more often in patients treated with active MCS devices compared with those without active MCS devices (142 of 197, 72% vs. 374 of 827, 45%; p < 0.001). All-cause mortality and bleeding rates were significantly higher in the active MCS group (all p < 0.001). After multivariable adjustment, the use of active MCS was significantly associated with the primary endpoint (odds ratio (OR) 4.0, 95% confidence interval (CI) 2.7–5.9; p < 0.001). Conclusions: In the CULPRIT-SHOCK trial, active MCS devices were used in approximately one fifth of patients. Patients treated with active MCS devices showed worse outcome at 30 days and 1 year

Modelling energy consumption of network transfers and virtual machine migration

Reducing energy consumption has become a key issue for data centres, not only because of economical benefits but also for environmental and marketing reasons. Therefore, assessing their energy consumption requires precise models. In the past years, many models targeting different hardware components, such as CPU, storage and network interface cards (NIC) have been proposed. However, most of them neglect energy consumption related to VM migration. Since VM migration is a network-intensive process, to accurately model its energy consumption we also need energy models for network transfers, comprising their complete software stacks with different energy characteristics. In this work, we present a comparative analysis of the energy consumption of the software stack of two of today's most used NICs in data centres, Ethernet and Infiniband. We carefully design for this purpose a set of benchmark experiments to assess the impact of different traffic patterns and interface settings on energy consumption. Using our benchmark results, we derive an energy consumption model for network transfers. Based on this model, we propose an energy consumption model for VM migration providing accurate predictions for paravirtualised VMs running on homogeneous hosts. We present a comprehensive analysis of our model on different machine sets and compare it with other models for energy consumption of VM migration, showing an improvement of up to 24% in accuracy, according to the NRMSE error metric. © 2015 Elsevier B.V

UniBench: A Benchmark for Multi-Model Database Management Systems

Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at http://udbms.cs.helsinki.fi/bench/.Peer reviewe

Large scale data warehouses on grid: Oracle database 10g and HP ProLiant systems

Grid computing has the potential of drastically changing enterprise computing as we know it today. The main concept of Grid computing is to see computing as a utility. It should not matter where data resides, or what computer processes a task. This concept has been applied successfully to academic research. It also has many advantages for commercial data warehouse applications such as virtualization, flexible provisioning, reduced cost due to commodity hardware, high availability and high scale-out. In this paper we show how a large-scale, high performing and scalable Grid based data warehouse can be implemented using commodity hardware (industry standard x86based), Oracle Database 10G and Linux operating system. We further demonstrate this architecture in a recently published TPC-H benchmark. 1