GenBase: A Complex Analytics Genomics Benchmark

This paper introduces a new benchmark, designed to test database management system (DBMS) performance on a mix of data management tasks (joins, filters, etc.) and complex analytics (regression, singular value decomposition, etc.) Such mixed workloads are prevalent in a number of application areas, including most science workloads and web analytics. As a specific use case, we have chosen genomics data for our benchmark, and have constructed a collection of typical tasks in this area. In addition to being representative of a mixed data management and analytics workload, this benchmark is also meant to scale to large dataset sizes and multiple nodes across a cluster. Besides presenting this benchmark, we have run it on a variety of storage systems including traditional row stores, newer column stores, Hadoop, and an array DBMS. We present performance numbers on all systems on single and multiple nodes, and show that performance differs by orders of magnitude between the various solutions. In addition, we demonstrate that most platforms have scalability issues. We also test offloading the analytics onto a coprocessor. The intent of this benchmark is to focus research interest in this area; to this end, all of our data, data generators, and scripts are available on our web site

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Elastic database systems

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 131-139).Distributed on-line transaction processing (OLTP) database management systems (DBMSs) are a critical part of the operation of large enterprises. These systems often serve time-varying workloads due to daily, weekly or seasonal fluctuations in load, or because of rapid growth in demand due to a company's business success. In addition, many OLTP workloads are heavily skewed to "hot" tuples or ranges of tuples. For example, the majority of NYSE volume involves only 40 stocks. To manage such fluctuations, many companies currently provision database servers for peak demand. This approach is wasteful and not resilient to extreme skew or large workload spikes. To be both efficient and resilient, a distributed OLTP DBMS must be elastic; that is, it must be able to expand and contract its cluster of servers as demand fluctuates, and dynamically balance load as hot tuples vary over time. This thesis presents two elastic OLTP DBMSs, called E-Store and P-Store, which demonstrate the benefits of elasticity for distributed OLTP DBMSs on different types of workloads. E-Store automatically scales the database cluster in response to demand spikes, periodic events, and gradual changes in an application's workload, but it is particularly well-suited for managing hot spots. In contrast to traditional single-tier hash and range partitioning strategies, E-Store manages hot spots through a two-tier data placement strategy: cold data is distributed in large chunks, while smaller ranges of hot tuples are assigned explicitly to individual nodes. P-Store is an elastic OLTP DBMS that is designed for a subset of OLTP applications in which load varies predictably. For these applications, P-Store performs better than reactive systems like E-Store, because P-Store uses predictive modeling to reconfigure the system in advance of predicted load changes. The experimental evaluation shows the efficacy of the two systems under variations in load across a cluster of machines. Compared to single-tier approaches, E-Store improves throughput by up to 130% while reducing latency by 80%. On a predictable workload, P-Store outperforms a purely reactive system by causing 72% fewer latency violations, and achieves performance comparable to static allocation for peak demand while using 50% fewer servers.by Rebecca Taft.Ph. D

Predictive modeling for management of database resources in the cloud

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages [68]-[70]).Public cloud providers who support a Database-as-a-Service offering must efficiently allocate computing resources to each of their customers in order to reduce the total number of servers needed without incurring SLA violations. For example, Microsoft serves more than one million database customers on its Azure SQL Database platform. In order to avoid unnecessary expense and stay competitive in the cloud market, Microsoft must pack database tenants onto servers as efficiently as possible. This thesis examines a dataset which contains anonymized customer resource usage statistics from Microsoft's Azure SQL Database service over a three-month period in late 2014. Using this data, this thesis contributes several new algorithms to efficiently pack database tenants onto servers by collocating tenants with compatible usage patterns. An experimental evaluation shows that the placement algorithms, specifically the Scalar Static algorithm and the Dynamic algorithm, are able to pack databases onto half of the machines used in production while incurring fewer SLA violations. The evaluation also shows that with two different cost models these algorithms can save 80% of operational costs compared to the algorithms used in production in late 2014.by Rebecca Taft.S.M

Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation.

OBJECTIVE: Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS: The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS: The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION: Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION: As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations