International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The Telemetry Definition and Processing (TDAP II) application is a PC-based software tool that meets the varied needs - both now and into the 21st century - of instrumentation engineers, data analysts, test engineers, and project personnel in the Test and Evaluation (T&E) community. TDAP II uses state-of-the-art commercial software technology that includes a Microsoft Access 97Ô database and a Microsoft Visual BasicÔ Graphical User Interface (GUI) for users to view and navigate the database. Developed by the Test and Analysis Division of the 96th Communications Group for the tenants of the Air Armament Center (AAC), Eglin AFB Florida, TDAP II provides a centralized repository for both aircraft and weapons instrumentation descriptions and telemetry EU conversion calibrations. Operating in a client/server environment, TDAP II can be effectively used on a small or large network as well as on both a classified or unclassified Intranet or Internet. This paper describes the components and design of this application, along with its operational flexibility and varied uses resulting from the chosen commercial software technology.

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

We report on initial research on the concurrency control issue of compiled database applications. Such applications have a repository style of architecture in which a collection of software modules operate on a common database in terms of a set of predefined transaction types, an architectural view that is useful for the deployment of database technology to embedded control programs. We focus on decoupling concurrency control from any functionality relating to recovery. Such decoupling facilitates the compile-time query optimization. <br /><br /> Because it is the possibility of transaction aborts for deadlock resolution that makes the recovery subsystem necessary, we choose the deadlock-free tree locking (TL) scheme for our purpose. With the knowledge of transaction workload, efficacious lock trees for runtime control can be determined at compile-time. We have designed compile-time algorithms to generate the lock tree and other relevant data structures, and runtime locking/unlocking algorithms based on such structures. We have further explored how to insert the lock steps into the transaction types at compile time. <br /><br /> To conduct our simulation experiments to evaluate the performance of TL, we have designed two workloads. The first one is from the OLTP benchmark TPC-C. The second is from the open-source operating system MINIX. Our experimental results show TL produces better throughput than the traditional two-phase locking (2PL) when the transactions are write-only; and for main-memory data, TL performs comparably to 2PL even in workloads with many reads.

Many activities are comprised of temporally dependent events that must be executed in a specific chronological order. Supportive software applications must preserve these temporal dependencies. Whenever the processing of this type of an application includes transactions submitted to a database that is shared with other such applications, the transaction concurrency control mechanisms within the database must also preserve the temporal dependencies. A basis for preserving temporal dependencies is established by using (within the applications and databases) real-time timestamps to identify and order events and transactions. The use of optimistic approaches to transaction concurrency control can be undesirable in such situations, as they allow incorrect results for database read operations. Although the incorrectness is detected prior to transaction committal and the corresponding transaction(s) restarted, the impact on the application or entity that submitted the transaction can be too costly. Three transaction concurrency control algorithms are proposed in this dissertation. These algorithms are based on timestamp ordering, and are designed to preserve temporal dependencies existing among data-dependent transactions. The algorithms produce execution schedules that are equivalent to temporally ordered serial schedules, where the temporal order is established by the transactions' start times. The algorithms provide this equivalence while supporting currency to the extent out-of-order commits and reads. With respect to the stated concern with optimistic approaches, two of the proposed algorithms are risk-free and return to read operations only committed data-item values. Risk with the third algorithm is greatly reduced by its conservative bias. All three algorithms avoid deadlock while providing risk-free or reduced-risk operation. The performance of the algorithms is determined analytically and with experimentation. Experiments are performed using functional database management system models that implement the proposed algorithms and the well-known Conservative Multiversion Timestamp Ordering algorithm.

Data analysts today have at their disposal a seemingly endless supply of data and repositories hence, datasets from which to draw. New datasets become available daily thus making the choice of which dataset to use difficult. Furthermore, traditional data analysis has been conducted using structured data repositories such as relational database management systems (RDBMS). These systems, by their nature and design, prohibit duplication for indexed collections forcing analysts to choose one value for each of the available attributes for an item in the collection. Often analysts discover two or more datasets with information about the same entity. When combining this data and transforming it into a form that is usable in an RDBMS, analysts are forced to deconflict the collisions and choose a single value for each duplicated attribute containing differing values. This deconfliction is the source of a considerable amount of guesswork and speculation on the part of the analyst in the absence of professional intuition. One must consider what is lost by discarding those alternative values. Are there relationships between the conflicting datasets that have meaning? Is each dataset presenting a different and valid view of the entity or are the alternate values erroneous? If so, which values are erroneous? Is there a historical significance of the variances? The analysis of modern datasets requires the use of specialized algorithms and storage and retrieval mechanisms to identify, deconflict, and assimilate variances of attributes for each entity encountered. These variances, or versions of attribute values, contribute meaning to the evolution and analysis of the entity and its relationship to other entities. A new, distinct storage and retrieval mechanism will enable analysts to efficiently store, analyze, and retrieve the attribute versions without unnecessary complexity or additional alterations of the original or derived dataset schemas. This paper presents technologies and innovations that assist data analysts in discovering meaning within their data and preserving all of the original data for every entity in the RDBMS.

Many modern applications process queries over unbounded streams of data. These applications include tracking financial data from international markets, intrusion detection in networks, monitoring remote sensors, and monitoring patients vital signs. These data streams arrive in real time, are unbounded in length and have unpredictable arrival patterns due to external uncontrollable factors such as network congestion or weather in the case of remote sensors. This thesis presents a novel technique for adapting the execution of stream queries that, to my knowledge, is not present in any other continuous query system to date. This thesis hypothesizes that utilizing a single scheduling algorithm to execute a continuous query, as is employed in other state-of-the-art continuous query systems, is not sufficient because existing scheduling algorithms all have inherent flaws or tradeoffs. Thus, one scheduling algorithm cannot optimally meet an arbitrary set of Quality of Service (QoS) requirements. Therefore, to meet unique features of specific monitoring applications, an adaptive strategy selector guidable by QoS requirements was developed. The adaptive strategy selector monitors the effects of its behavior on its environment through a feedback mechanism, with the aim of exploiting previously beneficial behavior and exploring alternative behavior. The feedback mechanism is guided by qualitatively comparing how well each algorithm has met the QoS requirements. Then the next scheduling algorithm is chosen by spinning a roulette wheel where each candidate is chosen with a probability equal to its performance score. The adaptive algorithm is general, being able to employ any candidate scheduling algorithm and to react to any combination of quality of service preferences. As part of this thesis, the Raindrop system was developed as exploratory test bed in which to conduct an experimental study. In that experimental study, the adaptive algorithm was shown to be effective in outperforming single scheduling algorithms for many QoS combinations and data arrival patterns.

With the explosive development of the mobile world, web applications and Big Data, new requirements for storage capacity and speed of database systems have arisen. The traditional relational database that has long dominated the marked has received competition because of its lack in speed and scalability. NoSQL is a collective name for databases that are not based on the traditional relational model. NoSQL databases are designed to easily expand their storage capacity while delivering high performance. NoSQL databases have been around for decades but the need for them is relatively new. Our partner expressed a desire to know what differences exist between NoSQL and the traditional relational database. To clarify these differences, we have answered the following questions in this work:  When can a NoSQL database be preferred to a relational database?  What are the differences in database performance? In order to answer these questions, a literature study has been conducted together with experiments where we test which performance differences exist between the selected databases. Performance tests have been performed with the benchmarking tool Yahoo Cloud Serving Benchmark, to verify or falsify the enhanced performance of the NoSQL databases. The hypotheses were falsified in both NoSQL databases. The results showed that the relational database performed better than the cloud based NoSQL databases, but also that the relational database performance deteriorates when the load increased. The results of the experiments are combined with the literature study and together answer our questions. The conclusion is that no database performs better than another one, it is the requirements of the data to be stored. From these requirements, analyses can be made to draw conclusions about what kind of database is preferable.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

The goal of this study is to increase understanding of information problems as they are revealed in interactions among users and search intermediaries during information retrieval. Specifically, this study seeks to investigate: (a) how interaction between users and search intermediaries reveals aspects of user information problems; (b) to explore the concept of representation with respect to information problems in interactive information retrieval; and (c) how user and search intermediaries focus on aspects of user information problems during the course of searches. This project extends research on interactive information retrieval, and presents a theoretical framework that synthesizes rational and non-rational questions concerning mental representation as it pertains to user's understanding of information problems.

The past few years have brought a major surge in the volumes of collected data. More and more enterprises and research institutions find tremendous value in data analysis and exploration. Big Data analytics is used for improving customer experience, perform complex weather data integration and model prediction, as well as personalized medicine and many other services. Advances in technology, along with high interest in big data, can only increase the demand on data collection and mining in the years to come. As a result, and in order to keep up with the data volumes, data processing has become increasingly distributed. However, most of the distributed processing for large data is done by batch processing and interactive exploration is hardly an option. To efficiently support queries over large amounts of data, appropriate indexing mechanisms must be in place. This dissertation proposes an indexing and query processing framework that can run on top of a distributed computing engine, to support fast, interactive data explorations in data warehouses. Our data processing layer is built around bit-vector based indices. This type of indexing features fast bit-wise operations and scales up well for high dimensional data. Additionally, compression can be applied to reduce the index size, and thus utilize less memory and network communication. Our work can be divided into two areas: index compression and query processing. Two compression schemes are proposed for sparse and dense bit-vectors. The design of these encoding methods is hardware-driven, and the query processing is optimized for the available computing hardware. Query algorithms are proposed for selection, aggregation, and other specialized queries. The query processing is supported on single machines, as well as computer clusters.