A parameterized model for selecting the optimum file organization in multi-attribute retrieval systems.

Massachusetts Institute of Technology, Alfred P. Sloan School of Management. Thesis. 1974. M.S.MICROFICHE COPY ALSO AVAILABLE IN DEWEY LIBRARY.Bibliography: leaves 135-142.M.S

Cost-Effective GNSS Hardware for High-Accuracy Surveys and Its Prospects for Post-Processed Kinematic (PPK) and Precise Point Positioning (PPP) Strategies

This dissertation determines for the first time the vertical accuracy achievable with low-cost mass-market multi-frequency, multi-GNSS (LM3GNSS) receivers, and antennas in the context of Ellipsoid Reference Survey (ERS), usually employed in bathymetric operations aboard survey platforms. LM3GNSS receivers are relatively new in the market, and their emergence is driven by the automobile industry and several mass-market applications requiring location-based solutions at high accuracies. It is foreseeable that emerging hydrographic survey platforms such as autonomous surface vehicles, small unmanned aircraft, crowd-sourced bathymetric platforms, and offshore GNSS buoy will find LM3GNSS receivers attractive since they are power- and cost-effective (often less than $1,000 per unit). Previous studies have shown that some mass-market GNSS receivers\u27 positioning accuracy is at the sub-meter level in some positioning strategies, but the authors rarely discussed the vertical accuracy. In rare cases where attention is given to the vertical component, the experiment design did not address the dynamic antenna scenario typical of hydrographic survey operations and the positioning performance that meets the hydrographic survey community\u27s aspirations. The LM3GNSS receivers and low-cost antennas considered in this dissertation achieved vertical accuracies within 0.15 m at a 95% confidence level in simulated precise point positioning (PPP) and post-processed kinematic positioning strategies. This dissertation characterizes the signal strength, multipath, carrier-phase residuals, and code residuals in the measurement quality assessment of four LM3GNSS receivers and four low-cost antennas. The dissertation investigates the performances of the LM3GNSS receivers and low-cost antennas in different antenna-receiver pairings, relative to a high-grade GNSS receiver and antenna in simulated-kinematic and precise point positioning (PPP) strategies. This dissertation also shows that solutions with an uncalibrated antenna improve with a cloned ANTEX file making the results comparable to those achieved with high-end GNSS antenna. This dissertation also describes a GNSS processing tool (with graphic user interface), developed from scratch by the author, that implements, among others, orbit interpolation and geodetic computations as steps towards multipath computation and analysis. The dissertation concludes as follows: (1) The LM3GNSS hardware considered in this dissertation provides effective alternative positioning and navigation performance for emerging survey platforms such as ASV and sUAS. (2) LM3GNSS hardware can meet vertical positioning accuracy on the order of 0.15 m at a 95% confidence level in PPP strategy on less dynamic platforms. (3) LM3GNSS receivers can provide PPK solutions at medium (30 – 40 km) baselines with a vertical positioning accuracy better than 0.15m at a 95% confidence level. (4) LM3GNSS receivers in PPP strategy should meet IHO S-44 order-1 and order-2 in shallow waters. (5) Zephyr3 antenna, being a high-end GNSS antenna, may not always offer the best performance with the LM3GNSS receiver, especially in a dynamic environment. (6) Given the current tracking capabilities, the measurement quality, and positioning performances of LM3GNSS receivers relative to the geodetic grade receiver, it is foreseeable that the distinction between high-end GNSS and LM3GNSS receivers will most likely fade away as GNSS hardware technology advances. (7) Maximizing an LM3GNSS receiver in PPK strategy requires a multi-constellation-enabled reference station and high (i.e., 1 Hz) data tracking rate; otherwise, the PPK solutions will likely drift up to 20 cm

Preliminary design review package for the solar heating and cooling central data processing system

The Central Data Processing System (CDPS) is designed to transform the raw data collected at remote sites into performance evaluation information for assessing the performance of solar heating and cooling systems. Software requirements for the CDPS are described. The programming standards to be used in development, documentation, and maintenance of the software are discussed along with the CDPS operations approach in support of daily data collection and processing

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science

Resurrection: Rethinking Magnetic Tapes For Cost Efficient Data Preservation

With the advent of Big Data technologies-the capacity to store and efficiently process large sets of data, doors of opportunities for developing business intelligence that was previously unknown, has opened. Each phase in the processing of this data requires specialized infrastructures. One such phase, the preservation and archiving of data, has proven its usefulness time and again. Data archives are processed using novel data mining methods to elicit vital data gathered over long periods of time and efficiently audit the growth of a business or an organization. Data preservation is also an important aspect of business processes which helps in avoiding loss of important information due to system failures, human errors and natural calamities. This thesis investigates the need, discusses possibilities and presents a novel, highly cost-effective, unified, long- term storage solution for data. Some of the common processes followed in large-scale data warehousing systems are analyzed for overlooked, inordinate shortcomings and a profitably feasible solution is conceived for them. The gap between the general needs of 'efficient' long-term storage and common, current functionalities is analyzed. An attempt to bridge this gap is made through the use of a hybrid, hierarchical media based, performance enhancing middleware and a monolithic namespace filesystem in a new storage architecture, Tape Cloud. The scope of studies carried out by us involves interpreting the effects of using heterogeneous storage media in terms of operational behavior, average latency of data transactions and power consumption. The results show the advantages of the new storage system by demonstrating the difference in operating costs, personnel costs and total cost of ownership from varied perspectives in a business model.Computer Science, Department o

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

System design package for the solar heating and cooling central data processing system

The central data processing system provides the resources required to assess the performance of solar heating and cooling systems installed at remote sites. These sites consist of residential, commercial, government, and educational types of buildings, and the solar heating and cooling systems can be hot-water, space heating, cooling, and combinations of these. The instrumentation data associated with these systems will vary according to the application and must be collected, processed, and presented in a form which supports continuity of performance evaluation across all applications. Overall software system requirements were established for use in the central integration facility which transforms raw data collected at remote sites into performance evaluation information for assessing the performance of solar heating and cooling systems