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XML is an emerging standard format for data on the Web as well as in business applications. In order to store and access this information in an efficient manner, database technology must be utilized. A relational database system, the most established and mature technology for query processing and storage, creates a strong foundation for such an XML data management system. However, while relational databases are based on SQL queries, the original user queries are written in XQuery, an XML query language. This XML query language has support for order-sensitive queries as XML is an order-sensitive markup language. A major problem has been discovered with loading XML in a relational database. That problem is the lack of native SQL support for and management of order handling. While XQuery has order and positional support, SQL does not have the same support. For example, individuals who were viewing XML information about music albums would have a hard time querying for the first three songs of a track list from a relational backend. Mapping XML documents to relational backends also proves hard as the data models (hierarchical elements versus flat tables) are so different. For these reasons, and other purposes, the Rainbow System is being developed at WPI as a system that bridges XML data and relational data. This thesis in particular deals with the algebra operators that affect order, order sensitive loading and mapping of XML documents, and the pushdown of order handling into SQL-capable query engines. The contributions of the thesis are the order-sensitive rewrite rules, new XML to relational mappings with different order styles, order-sensitive template-driven SQL generation, and a proposed metadata table for order-sensitive information. A system that implements these proposed techniques with XQuery as the XML query language and Oracle as the backend relational storage system has been developed. Experiments were created to measure execution time based on various factors. First, scalability of the system as backend data set size grows is studied. Second, scalability of the system as results returned from the database grows, and finally, query execution times with different loading types are explored. The experimental results are encouraging. Query execution with the relational backend proves to be much faster than native execution within the Rainbow system. These results confirm the practical utility of our proposed order-sensitive XQuery execution solution over relational data.

Natural language understanding has been a persistent challenge to researchers in various computer science fields, in a number of applications ranging from user support systems to entertainment and online teaching. A long term goal of the Artificial Intelligence field is to implement mechanisms that enable computers to emulate human dialogue. The recently developed ALICEbots, virtual agents with underlying AIML scripts, by A.L.I.C.E. foundation, use AIML scripts - a subset of XML - as the underlying pattern database for question answering. Their goal is to enable pattern-based, stimulus-response knowledge content to be served, received and processed over the Web, or offline, in the manner similar to HTML and XML. In this thesis, we describe a system that converts the AIML scripts to Prolog clauses and reuses them as part of a knowledge processor. The inference mechanism developed in this thesis is able to successfully match the input pattern with our clauses database even if words are missing. We also emulate the pattern deduction algorithm of the original logic deduction mechanism. Our rules, compatible with Semantic Web standards, bring structure to the meaningful content of Web pages and support interactive content retrieval using natural language.

Data security has been identified as one of the most important concerns where sensitive messages are exchanged over the network. In web service architecture, multiple distributed applications communicate with each other over the network by sending XML messages. How can we protect these sensitive messages? Some web services standards have emerged to tackle this problem. The XML Encryption standard defines the process of encrypting and decrypting all of an XML message, part of an XML message, or even an external resource. Like XML Encryption, the XML Signature standard specifies how to digitally sign an entire XML message, part of an XML message, or an external object. WS-Security defines how to embed security tokens, XML encryption, and XML signature into XML documents. It does not define new security mechanisms, but leverages existing security technologies such as encryption and digital signature. / by Keiko Hashizume. / Thesis (M.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

With the advent of XML, great challenges arise from the demand for efficiently retrieving information from remote XML sources across the Internet. The semantic caching technology can help to improve the efficiency of XML query processing in the Web environment. Different from the traditional tuple or page-based caching systems, semantic caching systems exploit the idea of reusing cached query results to answer new queries based on the query containment and rewriting techniques. Fundamental results on the containment of relational queries have been established. In the XML setting, the containment problem remains unexplored for comprehensive XML query languages such as XQuery, and little has been studied with respect to the cache management issue such as replacement. Hence, this dissertation addresses two issues fundamental to building an XQuery-based semantic caching system: XQuery containment and rewriting, and an effective replacement strategy. We first define a restricted XQuery fragment for which the containment problem is tackled. For two given queries $Q1$ and $Q2$, a preprocessing step including variable minimization and query normalization is taken to transform them into a normal form. Then two tree structures are constructed for respectively representing the pattern matching and result construction components of the query semantics. Based on the tree structures, query containment is reduced to tree homomorphism, with some specific mapping conditions. Important notations and theorems are also presented to support our XQuery containment and rewriting approaches. For the cache replacement, we propose a fine-grained replacement strategy based on the detailed user access statistics recorded on the internal XML view structure. As a result, less frequently used XML view fragments are replaced to achieve a better utilization of the cache space. Finally, we has implemented a semantic caching system called ACE-XQ to realize the proposed techniques. Case studies are conducted to confirm the correctness of our XQuery containment and rewriting approaches by comparing the query results produced by utilizing ACE-XQ against those by the remote XQuery engine. Experimental studies show that the query performance is significantly improved by adopting ACE-XQ, and that our partial replacement helps to enhance the cache hits and utilization comparing to the traditional total replacement.

Many applications are migrating or beginning to make use native XML data. We anticipate that queries will emerge that emphasize the structural semantics of XML query languages like XPath and XQuery. This brings a need for an efficient query engine and database management system tailored for XML data similar to traditional relational engines. While mapping large XML documents into relational database systems while possible, poses difficulty in mapping XML queries to the less powerful relational query language SQL and creates a data model mismatch between relational tables and semi-structured XML data. Hence native solutions to efficiently store and query XML data are being developed recently. However, most of these systems thus far fail to demonstrate scalability with large document sizes, to provide robust support for the XPath query language nor to adequately address costing with respect to query optimization. In this thesis, we propose a novel cost-driven XPath engine to support the scalable evaluation of ad-hoc XPath expressions called VAMANA. VAMANA makes use of an efficient XML repository for storing and indexing large XML documents called the Multi-Axis Storage Structure (MASS) developed at WPI. VAMANA extensively uses indexes for query evaluation by considering index-only plans. To the best of our knowledge, it is the only XML query engine that supports an index plan approach for large XML documents. Our index-oriented query plans allow queries to be evaluated while reading only a fraction of the data, as all tuples for a particular context node are clustered together. The pipelined query framework minimizes the cost of handing intermediate data during query processing. Unlike other native solutions, VAMANA provides support for all 13 XPath axes. Our schema independent cost model provides dynamically calculated statistics that are then used for intelligent cost-based transformations, further improving performance. Our optimization strategy for increasing execution time performance is affirmed through our experimental studies on XMark benchmark data. VAMANA query execution is significantly faster than leading available XML query engines.

"XML Stream Processing is an emerging technology designed to support declarative queries over continuous streams of data. The interest in this novel technology is growing due to the increasing number of real world applications such as monitoring systems for stock, email, and sensor data that need to analyze incoming data streams. There are however several open challenges. One, we must develop efficient techniques for pattern matching over the nested tag structure of XML as data streams in token by token. Two, we must develop techniques for query optimization to cope with complex user queries while given only incomplete knowledge of source data. When considering these challenges separately, then automata models have been shown by several recent works to be suited to tackle the first problem, while algebraic query models have been regarded as appropriate foundations to tackle the second problem. The question however remains how best to put these two models together to have an overall effective system. This thesis aims to exactly fill this gap. We propose a unified query framework to augment automata-style processing with algebra-based query optimization capabilities. We use the automata model to handle the token-oriented streaming XML data and use the algebraic model to support set-oriented optimization techniques. The framework has been designed in two layers such that the logical layer provides a uniform abstraction across the two models and any optimization techniques can be applied in either model uniformly using query rewritings. The physical layer, on the other hand, allows us to refine the implementation details after the logical layer optimization. We have successfully applied this framework in the Raindrop stream processing system. We have identified several trade-offs regarding which query functionality should be realized in which specific query model. We have developed novel optimization techniques to exploit these trade-offs. For example, a query rewrite rule can flexibly push down a pattern matching into the automata model when the optimizer decides that it is more efficient to do so. To deal with incomplete knowledge of source data, we have also developed novel techniques to monitor data statistics, based on which we can apply optimization techniques to choose the optimal query plan at runtime. Our experimental study confirms that considerable performance gains are being achieved when these optimization techniques are applied in our system."

In recent years XML, the eXtensible Markup Language has become the de-facto standard for publishing and exchanging information on the web and in enterprise data integration systems. Materialized views are often used in information integration systems to present a unified schema for efficient querying of distributed and possibly heterogenous data sources. On similar lines, ACE-XQ, an XQuery based semantic caching system shows the significant performance gains achieved by caching query results (as materialized views) and using these materialized views along with query containment techniques for answering future queries over distributed XML data sources. To keep data in these materialized views of ACE-XQ up-to-date, the view must be maintained i.e. whenever the base data changes, the corresponding cached data in the materialized view must also be updated. This thesis builds on the query containment ideas of ACE-XQ and proposes an efficient approach for self-maintenance of materialized views. Our experimental results illustrate the significant performance improvement achieved by this strategy over view re-computation for a variety of situations.

Due to the wide acceptance of the Word Wide Web Consortium (W3C) XPath language specification, native indexing for XML is needed to support path expression queries efficiently. XPath describes the different document tree relationships that may be queried as a set of axes. Many recent proposals for XML indexing focus on accelerating only a small subset of expressions possible using these axes. In particular, queries by ordinal position and updates that alter document structure are not well supported. A more general indexing solution is needed that not only offers efficient evaluation of all of the XPath axes, but also allows for efficient document update. We introduce MASS, a Multiple Axis Storage Structure, to meet the performance challenge posed by the XPath language. MASS is a storage and indexing solution for large XML documents that eliminates the need for external secondary storage. It is designed around the XPath language, providing efficient interfaces for evaluating all XPath axes. The clustered organization of MASS allows several different axes to be evaluated using the same index structure. The clustering, in conjunction with an internal compression mechanism exploiting specific XML characteristics, keep the size of the structure small which further aids efficiency. MASS introduces a versatile scheme for representing document node relationships that always allows for efficient updates. Finally, the integration of a ranked B+ tree allows MASS to efficiently evaluate XPath axes in large documents. We have implemented MASS in C++ and measured the performance of many different XPath expressions and document updates. Our experimental evaluation illustrates that MASS exhibits excellent performance characteristics for both queries and updates and scales well to large documents, making it a practical solution for XML storage. In conjunction with text indexing, MASS provides a complete solution from XML indexing.