Scaling geospatial searches in large spatial databases

Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: ���Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000���. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to advances in geolocation technologies (e.g., GPS-enabled smartphones) that allow users to associate location data to objects or events. The latter poses potential data ingestion challenges of large data volumes for practical GIS databases. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre-processing task) can be scaled in MapReduce���a widely-adopted parallel programming model for data intensive problems. The evaluation of our algorithms in a Hadoop cluster showed close to linear scalability in building R-tree indexes. Subsequently, we develop efficient algorithms for processing spatial queries with aspatial conditions. Novel techniques for simultaneously indexing spatial with textual and numeric data are developed to that end. Experimental evaluations with real-world, large spatial datasets measured query response times within the sub-second range for most cases, and up to a few seconds for a small number of cases, which is reasonable for interactive applications. Overall, the previous results show that the MapReduce parallel model is suitable for indexing tasks in spatial databases, and the adequate combination of spatial and aspatial attribute indexes can attain acceptable response times for interactive spatial queries with constraints on aspatial data.

ASDN : Automated Software Design Notebook tool

Software Engineering is one of the most widely researched areas of Computer Science. The ability to reuse software, much like reuse of hardware components is one of the key issues in software development. The object-oriented programming methodology is revolutionary in that it promotes software reusability. This thesis describes the development of a tool that helps programmers to design and implement software from within the Smalltalk Environment (an Object- Oriented programming environment). The ASDN tool is part of the PEREAM (Programming Environment for the Reuse and Evolution of Abstract Models) system, which advocates incremental development of software. The Asdn tool along with the PEREAM system seeks to enhance the Smalltalk programming environment by providing facilities for structured development of abstractions (concepts). It produces a document that describes the abstractions that are developed using this tool. The features of the ASDN tool are illustrated by an example.

Practical secure information flow in programming languages

If we classify variables in a program into various security levels, then a secure information flow analysis aims to verify statically that information in a program can flow only in ways consistent with the specified security levels. One well-studied approach is to formulate the rules of the secure information flow analysis as a type system. A major trend of recent research focuses on how to accommodate various sophisticated modern language features. However, this approach often leads to overly complicated and restrictive type systems, making them unfit for practical use. Also, problems essential to practical use, such as type inference and error reporting, have received little attention. This dissertation identified and solved major theoretical and practical hurdles to the application of secure information flow. ^ We adopted a minimalist approach to designing our language to ensure a simple lenient type system. We started out with a small simple imperative language and only added features that we deemed most important for practical use. One language feature we addressed is arrays. Due to the various leaking channels associated with array operations, arrays have received complicated and restrictive typing rules in other secure languages. We presented a novel approach for lenient array operations, which lead to simple and lenient typing of arrays. ^ Type inference is necessary because usually a user is only concerned with the security types for input/output variables of a program and would like to have all types for auxiliary variables inferred automatically. We presented a type inference algorithm B and proved its soundness and completeness. Moreover, algorithm B stays close to the program and the type system and therefore facilitates informative error reporting that is generated in a cascading fashion. Algorithm B and error reporting have been implemented and tested. ^ Lastly, we presented a novel framework for developing applications that ensure user information privacy. In this framework, core computations are defined as code modules that involve input/output data from multiple parties. Incrementally, secure flow policies are refined based on feedback from the type checking/inference. Core computations only interact with code modules from involved parties through well-defined interfaces. All code modules are digitally signed to ensure their authenticity and integrity. ^

An innovative approach to teaching structural induction for computer science

Proofs by induction are central to many computer science areas such as data structures, theory of computation, programming languages, program efficiency-time complexity, and program correctness. Proofs by induction can also improve students��� understanding and performance of computer science concepts such as programming languages, algorithm design, and recursion, as well as serve as a medium for teaching them. Even though students are exposed to proofs by induction in many courses of their curricula, they still have difficulties understanding and performing them. This impacts the whole course of their studies, since proofs by induction are omnipresent in computer science. Specifically, students do not gain conceptual understanding of induction early in the curriculum and as a result, they have difficulties applying it to more advanced areas later on in their studies. The goal of my dissertation is twofold: (1) identifying sources of computer science students��� difficulties with proofs by induction, and (2) developing a new approach to teaching proofs by induction by way of an interactive and multimodal electronic book (e-book). For the first goal, I undertook a study to identify possible sources of computer science students��� difficulties with proofs by induction. Its results suggest that there is a close correlation between students��� understanding of inductive definitions and their understanding and performance of proofs by induction. For designing and developing my e-book, I took into consideration the results of my study, as well as the drawbacks of the current methodologies of teaching proofs by induction for computer science. I designed my e-book to be used as a standalone and complete educational environment. I also conducted a study on the effectiveness of my e-book in the classroom. The results of my study suggest that, unlike the current methodologies of teaching proofs by induction for computer science, my e-book helped students overcome many of their difficulties and gain conceptual understanding of proofs induction.

A large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling

With the exponential increasing demands and uses of GIS data visualization system, such as urban planning, environment and climate change monitoring, weather simulation, hydrographic gauge and so forth, the geospatial vector and raster data visualization research, application and technology has become prevalent. However, we observe that current web GIS techniques are merely suitable for static vector and raster data where no dynamic overlaying layers. While it is desirable to enable visual explorations of large-scale dynamic vector and raster geospatial data in a web environment, improving the performance between backend datasets and the vector and raster applications remains a challenging technical issue. This dissertation is to implement these challenging and unimplemented areas: how to provide a large-scale dynamic vector and raster data visualization service with dynamic overlaying layers accessible from various client devices through a standard web browser, and how to make the large-scale dynamic vector and raster data visualization service as rapid as the static one. To accomplish these, a large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling and a comprehensive performance improvement solution are proposed, designed and implemented. They include: the quadtree-based indexing and parallel map tiling, the Legend String, the vector data visualization with dynamic layers overlaying, the vector data time series visualization, the algorithm of vector data rendering, the algorithm of raster data re-projection, the algorithm for elimination of superfluous level of detail, the algorithm for vector data gridding and re-grouping and the cluster servers side vector and raster data caching.

Towards simulation and emulation of large-scale computer networks

Developing analytical models that can accurately describe behaviors of Internet-scale networks is difficult. This is due, in part, to the heterogeneous structure, immense size and rapidly changing properties of today's networks. The lack of analytical models makes large-scale network simulation an indispensable tool for studying immense networks. However, large-scale network simulation has not been commonly used to study networks of Internet-scale. This can be attributed to three factors: 1) current large-scale network simulators are geared towards simulation research and not network research, 2) the memory required to execute an Internet-scale model is exorbitant, and 3) large-scale network models are difficult to validate. This dissertation tackles each of these problems. ^ First, this work presents a method for automatically enabling real-time interaction, monitoring, and control of large-scale network models. Network researchers need tools that allow them to focus on creating realistic models and conducting experiments. However, this should not increase the complexity of developing a large-scale network simulator. This work presents a systematic approach to separating the concerns of running large-scale network models on parallel computers and the user facing concerns of configuring and interacting with large-scale network models. ^ Second, this work deals with reducing memory consumption of network models. As network models become larger, so does the amount of memory needed to simulate them. This work presents a comprehensive approach to exploiting structural duplications in network models to dramatically reduce the memory required to execute large-scale network experiments. ^ Lastly, this work addresses the issue of validating large-scale simulations by integrating real protocols and applications into the simulation. With an emulation extension, a network simulator operating in real-time can run together with real-world distributed applications and services. As such, real-time network simulation not only alleviates the burden of developing separate models for applications in simulation, but as real systems are included in the network model, it also increases the confidence level of network simulation. This work presents a scalable and flexible framework to integrate real-world applications with real-time simulation.^

Algorithms for efficient transaction management and consistent queries in client -server semantic object-oriented parallel databases

Large read-only or read-write transactions with a large read set and a small write set constitute an important class of transactions used in such applications as data mining, data warehousing, statistical applications, and report generators. Such transactions are best supported with optimistic concurrency, because locking of large amounts of data for extended periods of time is not an acceptable solution. The abort rate in regular optimistic concurrency algorithms increases exponentially with the size of the transaction. The algorithm proposed in this dissertation solves this problem by using a new transaction scheduling technique that allows a large transaction to commit safely with significantly greater probability that can exceed several orders of magnitude versus regular optimistic concurrency algorithms. A performance simulation study and a formal proof of serializability and external consistency of the proposed algorithm are also presented.^ This dissertation also proposes a new query optimization technique (lazy queries). Lazy Queries is an adaptive query execution scheme which optimizes itself as the query runs. Lazy queries can be used to find an intersection of sub-queries in a very efficient way, which does not require full execution of large sub-queries nor does it require any statistical knowledge about the data.^ An efficient optimistic concurrency control algorithm used in a massively parallel B-tree with variable-length keys is introduced. B-trees with variable-length keys can be effectively used in a variety of database types. In particular, we show how such a B-tree was used in our implementation of a semantic object-oriented DBMS. The concurrency control algorithm uses semantically safe optimistic virtual "locks" that achieve very fine granularity in conflict detection. This algorithm ensures serializability and external consistency by using logical clocks and backward validation of transactional queries. A formal proof of correctness of the proposed algorithm is also presented. ^

An Innovative Approach to Teaching Structural Induction for Computer Science

Proofs by induction are central to many computer science areas such as data structures, theory of computation, programming languages, program efficiency-time complexity, and program correctness. Proofs by induction can also improve students��� understanding of and performance with computer science concepts such as programming languages, algorithm design, and recursion, as well as serve as a medium for teaching them. Even though students are exposed to proofs by induction in many courses of their curricula, they still have difficulties understanding and performing them. This impacts the whole course of their studies, since proofs by induction are omnipresent in computer science. Specifically, students do not gain conceptual understanding of induction early in the curriculum and as a result, they have difficulties applying it to more advanced areas later on in their studies. The goal of my dissertation is twofold: 1. identifying sources of computer science students��� difficulties with proofs by induction, and 2. developing a new approach to teaching proofs by induction by way of an interactive and multimodal electronic book (e-book). For the first goal, I undertook a study to identify possible sources of computer science students��� difficulties with proofs by induction. Its results suggest that there is a close correlation between students��� understanding of inductive definitions and their understanding and performance of proofs by induction. For designing and developing my e-book, I took into consideration the results of my study, as well as the drawbacks of the current methodologies of teaching proofs by induction for computer science. I designed my e-book to be used as a standalone and complete educational environment. I also conducted a study on the effectiveness of my e-book in the classroom. The results of my study suggest that, unlike the current methodologies of teaching proofs by induction for computer science, my e-book helped students overcome many of their difficulties and gain conceptual understanding of proofs induction.