Bioinformatics tools in predictive ecology: Applications to fisheries

This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse

SODA: Generating SQL for Business Users

The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need answers to ad-hoc queries that were not anticipated. This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.Comment: VLDB201

Automatic physical database design : recommending materialized views

This work discusses physical database design while focusing on the problem of selecting materialized views for improving the performance of a database system. We first address the satisfiability and implication problems for mixed arithmetic constraints. The results are used to support the construction of a search space for view selection problems. We proposed an approach for constructing a search space based on identifying maximum commonalities among queries and on rewriting queries using views. These commonalities are used to define candidate views for materialization from which an optimal or near-optimal set can be chosen as a solution to the view selection problem. Using a search space constructed this way, we address a specific instance of the view selection problem that aims at minimizing the view maintenance cost of multiple materialized views using multi-query optimization techniques. Further, we study this same problem in the context of a commercial database management system in the presence of memory and time restrictions. We also suggest a heuristic approach for maintaining the views while guaranteeing that the restrictions are satisfied. Finally, we consider a dynamic version of the view selection problem where the workload is a sequence of query and update statements. In this case, the views can be created (materialized) and dropped during the execution of the workload. We have implemented our approaches to the dynamic view selection problem and performed extensive experimental testing. Our experiments show that our approaches perform in most cases better than previous ones in terms of effectiveness and efficiency

Database Workload Management (Dagstuhl Seminar 12282)

This report documents the program and the outcomes of Dagstuhl Seminar 12282 "Database Workload Management". Dagstuhl Seminar 12282 was designed to provide a venue where researchers can engage in dialogue with industrial participants for an in-depth exploration of challenging industrial workloads, where industrial participants can challenge researchers to apply the lessons-learned from their large-scale experiments to multiple real systems, and that would facilitate the release of real workloads that can be used to drive future research, and concrete measures to evaluate and compare workload management techniques in the context of these workloads

A Service Late Binding Enabled Solution for Data Integration from Autonomous and Evolving Databases

Integrating data from autonomous, distributed and heterogeneous data sources to provide a unified vision is a common demand for many businesses. Since the data sources may evolve frequently to satisfy their own independent business needs, solutions which use hard coded queries to integrate participating databases may cause high maintenance costs when evolution occurs. Thus a new solution which can handle database evolution with lower maintenance effort is required. This thesis presents a new solution: Service Late binding Enabled Data Integration (SLEDI) which is set into a framework modeling the essential processes of the data integration activity. It integrates schematic heterogeneous relational databases with decreased maintenance costs for handling database evolution. An algorithm, named Information Provision Unit Describing (IPUD) is designed to describe each database as a set of Information Provision Units (IPUs). The IPUs are represented as Directed Acyclic Graph (DAG) structured data instead of hard coded queries, and further realized as data services. Hence the data integration is achieved through service invocations. Furthermore, a set of processes is defined to handle the database evolution through automatically identifying and modifying the IPUs which are affected by the evolution. An extensive evaluation based on a case study is presented. The result shows that the schematic heterogeneities defined in this thesis can be solved by IPUD except the relation isomorphism discrepancy. Ten out of thirteen types of schematic database evolution can be automatically handled by the evolution handling processes as long as the evolution is represented by the designed data model. The computational costs of the automatic evolution handling show a slow linear growth with the number of participating databases. Other characteristics addressed include SLEDI’s scalability, independence of application domain and databases model. The descriptive comparison with other data integration approaches shows that although the Data as a Service approach may result in lower performance under some circumstances, it supports better flexibility for integrating data from autonomous and evolving data sources

An Approach for Modeling and Ranking Node-level Stragglers in Cloud Datacenters

The ability of servers to effectively execute tasks within Cloud datacenters varies due to heterogeneous CPU and memory capacities, resource contention situations, network configurations and operational age. Unexpectedly slow server nodes (node-level stragglers) result in assigned tasks becoming task-level stragglers, which dramatically impede parallel job execution. However, it is currently unknown how slow nodes directly correlate to task straggler manifestation. To address this knowledge gap, we propose a method for node performance modeling and ranking in Cloud datacenters based on analyzing parallel job execution tracelog data. By using a production Cloud system as a case study, we demonstrate how node execution performance is driven by temporal changes in node operation as opposed to node hardware capacity. Different sample sets have been filtered in order to evaluate the generality of our framework, and the analytic results demonstrate that node abilities of executing parallel tasks tend to follow a 3-parameter-loglogistic distribution. Further statistical attribute values such as confidence interval, quantile value, extreme case possibility, etc. can also be used for ranking and identifying potential straggler nodes within the cluster. We exploit a graph-based algorithm for partitioning server nodes into five levels, with 0.83% of node-level stragglers identified. Our work lays the foundation towards enhancing scheduling algorithms by avoiding slow nodes, reducing task straggler occurrence, and improving parallel job performance

Cardinality estimation in ETL processes

The cardinality estimation in ETL processes is particularly difficult. Aside from the well-known SQL operators, which are also used in ETL processes, there are a variety of operators without exact counterparts in the relational world. In addition to those, we find operators that support very specific data integration aspects. For such operators, there are no well-examined statistic approaches for cardinality estimations. Therefore, we propose a black-box approach and estimate the cardinality using a set of statistic models for each operator. We discuss different model granularities and develop an adaptive cardinality estimation framework for ETL processes. We map the abstract model operators to specific statistic learning approaches (regression, decision trees, support vector machines, etc.) and evaluate our cardinality estimations in an extensive experimental study

A Design Framework for Efficient Distributed Analytics on Structured Big Data

Distributed analytics architectures are often comprised of two elements: a compute engine and a storage system. Conventional distributed storage systems usually store data in the form of files or key-value pairs. This abstraction simplifies how the data is accessed and reasoned about by an application developer. However, the separation of compute and storage systems makes it difficult to optimize costly disk and network operations. By design the storage system is isolated from the workload and its performance requirements such as block co-location and replication. Furthermore, optimizing fine-grained data access requests becomes difficult as the storage layer is hidden away behind such abstractions. Using a clean slate approach, this thesis proposes a modular distributed analytics system design which is centered around a unified interface for distributed data objects named the DDO. The interface couples key mechanisms that utilize storage, memory, and compute resources. This coupling makes it ideal to optimize data access requests across all memory hierarchy levels, with respect to the workload and its performance requirements. In addition to the DDO, a complementary DDO controller implementation controls the logical view of DDOs, their replication, and distribution across the cluster. A proof-of-concept implementation shows improvement in mean query time by 3-6x on the TPC-H and TPC-DS benchmarks, and more than an order of magnitude improvement in many cases