Query Interactions in Database Systems

The typical workload in a database system consists of a mix of multiple queries of different types, running concurrently and interacting with each other. The same query may have different performance in different mixes. Hence, optimizing performance requires reasoning about query mixes and their interactions, rather than considering individual queries or query types. In this dissertation, we demonstrate how queries affect each other when they are executing concurrently in different mixes. We show the significant impact that query interactions can have on the end-to-end workload performance. A major hurdle in the understanding of query interactions in database systems is that there is a large spectrum of possible causes of interactions. For example, query interactions can happen because of any of the resource-related, data-related or configuration-related dependencies that exist in the system. This variation in underlying causes makes it very difficult to come up with robust analytical performance models to capture and model query interactions. We present a new approach for modeling performance in the presence of interactions, based on conducting experiments to measure the effect of query interactions and fitting statistical models to the data collected in these experiments to capture the impact of query interactions. The experiments collect samples of the different possible query mixes, and measure the performance metrics of interest for the different queries in these sample mixes. Statistical models such as simple regression and instance-based learning techniques are used to train models from these sample mixes. This approach requires no prior assumptions about the internal workings of the database system or the nature or cause of the interactions, making it portable across systems. We demonstrate the potential of capturing, modeling, and exploiting query interactions by developing techniques to help in two database performance related tasks: workload scheduling and estimating the completion time of a workload. These are important workload management problems that database administrators have to deal with routinely. We consider the problem of scheduling a workload of report-generation queries. Our scheduling algorithms employ statistical performance models to schedule appropriate query mixes for the given workload. Our experimental evaluation demonstrates that our interaction-aware scheduling algorithms outperform scheduling policies that are typically used in database systems. The problem of estimating the completion time of a workload is an important problem, and the state of the art does not offer any systematic solution. Typically database administrators rely on heuristics or observations of past behavior to solve this problem. We propose a more rigorous solution to this problem, based on a workload simulator that employs performance models to simulate the execution of the different mixes that make up a workload. This mix-based simulator provides a systematic tool that can help database administrators in estimating workload completion time. Our experimental evaluation shows that our approach can estimate the workload completion times with a high degree of accuracy. Overall, this dissertation demonstrates that reasoning about query interactions holds significant potential for realizing performance improvements in database systems. The techniques developed in this work can be viewed as initial steps in this interesting area of research, with lots of potential for future work

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting

Visual Information Retrieval in Digital Libraries

The emergence of information highways and multimedia computing has resulted in redefining the concept of libraries. It is widely believed that in the next few years, a significant portion of information in libraries will be in the form of multimedia electronic documents. Many approaches are being proposed for storing, retrieving, assimilating, harvesting, and prospecting information from these multimedia documents. Digital libraries are expected to allow users to access information independent of the locations and types of data sources and will provide a unified picture of information. In this paper, we discuss requirements of these emerging information systems and present query methods and data models for these systems. Finally, we briefly present a few examples of approaches that provide a preview of how things will be done in the digital libraries in the near future.published or submitted for publicatio

Supporting Complex Scientific Database Schemas in a Grid Middleware

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” DOI: 10.1109/AINA.2009.129The volume of digital scientific data has increased considerably with advancing technologies of computing devices and scientific instruments. We are exploring the use of emerging Grid technologies for the management and manipulation of very large distributed scientific datasets. Taking as an example a terabyte-size scientific database with complex database schema, this paper focuses on the potential of a well-known Grid middleware - OGSA-DQP - for distributing such datasets. In particular, we investigate and extend the data type support in this system to handle a complex schema of a real scientific database - the Sloan Digital Sky Survey database