ShreX: managing XML documents in relational databases

Journal ArticleWe describe ShreX, a freely-available system for shredding, loading and querying XML documents in relational databases. ShreX supports all mapping strategies proposed in the literature as well as strategies available in commercial RDBMSs. It provides generic (mapping-independent) functions for loading shredded documents into relations and for translating XML queries into SQL. ShreX is portable and can be used with any relational database backend

XML Reconstruction View Selection in XML Databases: Complexity Analysis and Approximation Scheme

Query evaluation in an XML database requires reconstructing XML subtrees rooted at nodes found by an XML query. Since XML subtree reconstruction can be expensive, one approach to improve query response time is to use reconstruction views - materialized XML subtrees of an XML document, whose nodes are frequently accessed by XML queries. For this approach to be efficient, the principal requirement is a framework for view selection. In this work, we are the first to formalize and study the problem of XML reconstruction view selection. The input is a tree $T$, in which every node $i$ has a size $c_i$ and profit $p_i$, and the size limitation $C$. The target is to find a subset of subtrees rooted at nodes $i_1,\cdots, i_k$ respectively such that $c_{i_1}+\cdots +c_{i_k}\le C$, and $p_{i_1}+\cdots +p_{i_k}$ is maximal. Furthermore, there is no overlap between any two subtrees selected in the solution. We prove that this problem is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) as a solution

Why and How to Benchmark XML Databases

Benchmarks belong to the very standard repertory of tools deployed in database development. Assessing the capabilities of a system, analyzing actual and potential bottlenecks, and, naturally, comparing the pros and cons of different systems architectures have become indispensable tasks as databases management systems grow in complexity and capacity. In the course of the development of XML databases the need for a benchmark framework has become more and more evident: a great many different ways to store XML data have been suggested in the past, each with its genuine advantages, disadvantages and consequences that propagate through the layers of a complex database system and need to be carefully considered. The different storage schemes render the query characteristics of the data variably different. However, no conclusive methodology for assessing these differences is available to date. In this paper, we outline desiderata for a benchmark for XML databases drawing from our own experience of developing an XML repository, involvement in the definition of the standard query language, and experience with standard benchmarks for relational databases

Why and How to Benchmark XML Databases

Benchmarks belong to the very standard repertory of tools deployed in database development. Assessing the capabilities of a system, analyzing actual and potential bottlenecks, and, naturally, comparing the pros and cons of different systems architectures have become indispensable tasks as databases management systems grow in complexity and capacity. In the course of the development of XML databases the need for a benchmark framework has become more and more evident: a great many different ways to store XML data have been suggested in the past, each with its genuine advantages, disadvantages and consequences that propagate through the layers of a complex database system and need to be carefully considered. The different storage schemes render the query characteristics of the data variably different. However, no conclusive methodology for assessing these differences is available to date. In this paper, we outline desiderata for a benchmark for XML databases drawing from our own experience of developing an XML repository, involvement in the definition of the standard query language, and experience with standard benchmarks for relational databases

MORTAL: A Tool of Automatically Designing Relational Storage Schemas for Multi-model Data through Reinforcement Learning

Considering relational databases having powerful capabilities in handling security, user authentication, query optimization, etc., several commercial and academic frameworks reuse relational databases to store and query semi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, property graph). However, these works concentrate on managing one of the above data models with RDBMSs. That is, it does not exploit the underlying tools to automatically generate the relational schema for storing multi-model data. In this demonstration, we present a novel reinforcement learning-based tool called MORTAL. Specifically, given multi-model data containing different data models and a set of queries, it could automatically design a relational schema to store these data while having a great query performance. To demonstrate it clearly, we are centered around the following modules: generating initial state based on loaded multi-model data, influencing learning process by setting parameters, controlling generated relational schema through providing semantic constraints, improving the query performance of relational schema by specifying queries, and a highly interactive interface for showing query performance and storage consumption when users adjust the generated relational schema.Peer reviewe

Path Queries on Compressed XML

Central to any XML query language is a path language such as XPath which operates on the tree structure of the XML document. We demonstrate in this paper that the tree structure can be e#ectively compressed and manipulated using techniques derived from symbolic model checking . Specifically, we show first that succinct representations of document tree structures based on sharing subtrees are highly e#ective. Second, we show that compressed structures can be queried directly and e#ciently through a process of manipulating selections of nodes and partial decompression

An extended preorder index for optimising XPath expressions

Many of the problems with native XML databases relate to query performance and subsequently, it can be difficult to convince traditional database users of the benefits of using semi- or unstructured databases. Presently, there still lacks an index structure providing efficient support for structural queries and the traditional data-centric and content queries. This paper presents an extended index structure based on the preorder traversal rank and the level (or depth) rank of each node in a document tree. The extended index fully supports the navigation of all XPath axes while efficiently supporting data-centric queries. The ability to start path traversals from arbitrary nodes in a document tree also enables the extended index to support the evaluation of path traversals embedded in XQuery expressions. Furthermore, an encoding technique is presented where properties of the level ranking may be exploited to provide efficient and optimised level-based XPath evaluations

Okapi-based XML indexing

Purpose – Being an important data exchange and information storage standard, XML has generated a great deal of interest and particular attention has been paid to the issue of XML indexing. Clear use cases for structured search in XML have been established. However, most of the research in the area is either based on relational database systems or specialized semi‐structured data management systems. This paper aims to propose a method for XML indexing based on the information retrieval (IR) system Okapi. Design/methodology/approach – First, the paper reviews the structure of inverted files and gives an overview of the issues of why this indexing mechanism cannot properly support XML retrieval, using the underlying data structures of Okapi as an example. Then the paper explores a revised method implemented on Okapi using path indexing structures. The paper evaluates these index structures through the metrics of indexing run time, path search run time and space costs using the INEX and Reuters RVC1 collections. Findings – Initial results on the INEX collections show that there is a substantial overhead in space costs for the method, but this increase does not affect run time adversely. Indexing results on differing sized Reuters RVC1 sub‐collections show that the increase in space costs with increasing the size of a collection is significant, but in terms of run time the increase is linear. Path search results show sub‐millisecond run times, demonstrating minimal overhead for XML search. Practical implications – Overall, the results show the method implemented to support XML search in a traditional IR system such as Okapi is viable. Originality/value – The paper provides useful information on a method for XML indexing based on the IR system Okapi

Big Data Management for MMO Games and Integrated Website Implementation

With the popularity and success of massively multiplayer Games (MMOGs), the development of MMOGS has got a quantum leap on game's contents and entertainment which attract huge number of players making MMOGs these years a big business which increased to billions of dollars revenue each year worldwide. But with this number of players and these game contents, the data volume produced from games has rapidly increased and used by simultaneously game players around the world. This data require high performance, fault tolerance and scalability. Considering all these demands the popular used relational database becomes a big challenge and cannot overcomes the challenges and cannot meet the requirements for MMOGS data storage. This paper focus on using big data technology tools to completely meet the requirement of MMO games. My work can be divided into two parts: the first part we proposed Cassandra database for MMO games data storing and the integration of Hadoop with Cassandra nodes for high performance in operations process. The second part: we implement a new MMO website with new payment methods, new advertisement program by friend2019;s invitations and other enhanced function. By implementing this website and comparisons of results of our database management, we show the applicability of our approach as well as the relative performance benefits of designing new games or website using our architecture