Ranking model construction is an important topic in information retrieval. Recently, many approaches based on the idea of ¡§learning to rank¡¨ have been proposed for this task and most of them attempt to score all documents of different queries by resorting to a single function. In this thesis, we propose a novel framework of query-dependent ranking. A simple similarity measure is used to calculate similarities between queries. An individual ranking model is constructed for each training query with corresponding documents. When a new query is asked, documents retrieved for the new query are ranked according to the scores determined by a ranking model which is combined from the models of similar training queries. A mechanism for determining combining weights is also provided. Experimental results show that this query dependent ranking approach is more effective than other approaches.

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Answering object queries (i.e. instance retrieval) is a central task in ontology based data access (OBDA). Performing this task involves reasoning with respect to a knowledge base K (i.e. ontology) over some description logic (DL) dialect L. As the expressive power of L grows, so does the complexity of reasoning with respect to K. Therefore, eliminating the need to reason with respect to a knowledge base K is desirable. In this work, we propose an optimization to improve performance of answering object queries by eliminating the need to reason with respect to the knowledge base and, instead, utilizing cached query results when possible. In particular given a DL dialect L, an object query C over some knowledge base K and a set of cached query results S={S1, ..., Sn} obtained from evaluating past queries, we rewrite C into an equivalent query D, that can be evaluated with respect to an empty knowledge base, using cached query results S' = {Si1, ..., Sim}, where S' is a subset of S. The new query D is an interpolant for the original query C with respect to K and S. To find D, we leverage a tool for enumerating interpolants of a given sentence with respect to some theory. We describe a procedure that maps a knowledge base K, expressed in terms of a description logic dialect of first order logic, and object query C into an equivalent theory and query that are input into the interpolant enumerating tool, and resulting interpolants into an object query D that can be evaluated over an empty knowledge base. We show the efficacy of our approach through experimental evaluation on a Lehigh University Benchmark (LUBM) data set, as well as on a synthetic data set, LUBMMOD, that we created by augmenting an LUBM ontology with additional axioms.

This thesis presents strategies to improve the query evaluation performance over huge volumes of relational-like data that is stored in the memory space of managed applications. Storing and processing application data in the memory space of managed applications is motivated by the convergence of two recent trends in data management. First, dropping DRAM prices have led to memory capacities that allow the entire working set of an application to fit into main memory and to the emergence of in-memory database systems (IMDBs). Second, language-integrated query transparently integrates query processing syntax into programming languages and, therefore, allows complex queries to be composed in the application. IMDBs typically serve as data stores to applications written in an object-oriented language running on a managed runtime. In this thesis, we propose a deeper integration of the two by storing all application data in the memory space of the application and using language-integrated query, combined with query compilation techniques, to provide fast query processing. As a starting point, we look into storing data as runtime-managed objects in collection types provided by the programming language. Queries are formulated using language-integrated query and dynamically compiled to specialized functions that produce the result of the query in a more efficient way by leveraging query compilation techniques similar to those used in modern database systems. We show that the generated query functions significantly improve query processing performance compared to the default execution model for language-integrated query. However, we also identify additional inefficiencies that can only be addressed by processing queries using low-level techniques which cannot be applied to runtime-managed objects. To address this, we introduce a staging phase in the generated code that makes query-relevant managed data accessible to low-level query code. Our experiments in .NET show an improvement in query evaluation performance of up to an order of magnitude over the default language-integrated query implementation. Motivated by additional inefficiencies caused by automatic garbage collection, we introduce a new collection type, the black-box collection. Black-box collections integrate the in-memory storage layer of a relational database system to store data and hide the internal storage layout from the application by employing existing object-relational mapping techniques (hence, the name black-box). Our experiments show that black-box collections provide better query performance than runtime-managed collections by allowing the generated query code to directly access the underlying relational in-memory data store using low-level techniques. Black-box collections also outperform a modern commercial database system. By removing huge volumes of collection data from the managed heap, black-box collections further improve the overall performance and response time of the application and improve the application’s scalability when facing huge volumes of collection data. To enable a deeper integration of the data store with the application, we introduce self-managed collections. Self-managed collections are a new type of collection for managed applications that, in contrast to black-box collections, store objects. As the data elements stored in the collection are objects, they are directly accessible from the application using references which allows for better integration of the data store with the application. Self-managed collections manually manage the memory of objects stored within them in a private heap that is excluded from garbage collection. We introduce a special collection syntax and a novel type-safe manual memory management system for this purpose. As was the case for black-box collections, self-managed collections improve query performance by utilizing a database-inspired data layout and allowing the use of low-level techniques. By also supporting references between collection objects, they outperform black-box collections.

The ever-increasing density of computer storage devices has allowed the average user to store enormous quantities of multimedia content, and a large amount of this content is usually music. Current search techniques for musical content rely on meta-data tags which describe artist, album, year, genre, etc. Query-by-content systems, however, allow users to search based upon the actual acoustical content of the songs. Recent systems have mainly depended upon textual representations of the queries and targets in order to apply common string-matching algorithms and are often confined to a single query style (e.g., humming). These methods also lose much of the information content of the song which limits the ways in which a user may search. We present a query-by-content system which supports querying in several styles using a Self-Organizing Map as its basis. The results from testing our system show that it performs better than random orderings and is, therefore, a viable option for musical query-by-content.

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As digital images become ubiquitous in many applications, the need for efficient and effective retrieval techniques is more demanding than ever. Query by Example (QBE) and Query by Concept (QBC) are among the most popular query models. The former model accepts example images as queries and searches for similar ones based on low-level features such as colors and textures. The latter model allows queries to be expressed in the form of high-level semantics or concept words, such as "boat" or "car," and finds images that match the specified concepts. Recent research has focused on the connections between these two models and attempts to close the semantic-gap between them. This research involves finding the best method that maps a set of low-level features into high-level concepts. Automatic annotation techniques are investigated in this dissertation to facilitate QBC. In this approach, sets of training images are used to discover the relationship between low-level features and predetermined high-level concepts. The best mapping with respect to the training sets is proposed and used to analyze images, annotating them with the matched concept words. One principal difference between QBE and QBC is that, while similarity matching in QBE must be done at the query time, QBC performs concept exploration off-line. This difference allows QBC techniques to shift the time-consuming task of determining similarity away from the query time, thus facilitating the additional processing time required for increasingly accurate matching. Consequently, QBC's primary design objective is to achieve accurate annotation within a reasonable processing time. This objective is the guiding principle in the design of the following proposed methods which facilitate image annotation: 1.A novel dynamic similarity function. This technique allows users to query with multiple examples: relevant, irrelevant or neutral. It uses the range distance in each group to automatically determine weights in the distance function. Among the advantages of this technique are higher precision and recall rates with fast matching time. 2.Object recognition based on skeletal graphs. The topologies of objects' skeletal graphs are captured and compared at the node level. Such graph representation allows preservation of the skeletal graph's coherence without sacrificing the flexibility of matching similar portions of graphs across different levels. The technique is robust to translation, scaling, and rotation invariants at object level. This technique achieves high precision and recall rates with reasonable matching time and storage space. 3.ASIA (Automatic Sampling-based Image Annotation) is a technique based on a new sampling-based matching framework allowing users to identify their area of interest. ASIA eliminates noise, or irrelevant areas of the image. ASIA is robust to translation, scaling, and rotation invariants at the object level. This technique also achieves high precision and recall rates. While the above techniques may not be the fastest when contrasted with some other recent QBE techniques, they very effectively perform image annotation. The results of applying these processes are accurately annotated database images to which QBC may then be applied. The results of extensive experiments are presented to substantiate the performance advantages of the proposed techniques and allow them to be compared with other recent high-performance techniques. Additionally, a discussion on merging the proposed techniques into a highly effective annotation system is also detailed.

Current information retrieval models are optimized for retrieval with short keyword queries. In contrast, in this dissertation we focus on longer, verbose queries with more complex structure that are becoming more common in both mobile and web search. To this end, we propose an expressive query representation formalism based on query hypergraphs. Unlike the existing query representations, query hypergraphs model the dependencies between arbitrary concepts in the query, rather than dependencies between single query terms. Query hypergraphs are parameterized by importance weights, which are assigned to concepts and concept dependencies in the query hypergraph, based on their contribution to the overall retrieval effectiveness. Query hypergraphs are not limited to modeling the explicit query structure. Accordingly, we develop two methods for query expansion using query hypergraphs. In these methods, the expansion concepts in the query hypergraph may come either from the retrieval corpus alone or from a combination of multiple information sources such as Wikipedia or the anchor text extracted from a large-scale web corpus. We empirically demonstrate that query hypergraphs are consistently and significantly more effective than many of the current state-of-the-art retrieval methods, as demonstrated by the experiments on newswire and web corpora. Query hypergraphs improve the retrieval performance for all query types, and, in particular, they exhibit the highest effectiveness gains for verbose queries.

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GraphQL is a query language rising in popularity, causing many transitions from traditional API endpoints to a GraphQL solution. Reflecting upon the positives and the flaws to using GraphQL, a DataLoader for batching queries sent to databases is sold as a solution to the infamous N+1 problem. Experiments were conducted to test how GraphQL response time, with and without DataLoader, changes when paired with MySQL and MongoDB. Along with the experiments, a Literature Review was conducted reflecting over the databases structural differences that could affect the response time for GraphQL. Results suggest that no major differences were to be found, and the explanation for the minor differences could rather be because of the disparity in query optimization instead of architectural differences for MySQL and MongoDB.