Efficient storage of XML data

We introduce NATIX, an efficient, native repository for storing, retrieving and managing tree-structured large objects, preferably XML documents. In contrast to traditionallarge object (LOB) managers, we do not split at arbitrary byte positions but take the semantics of the underlying tree structure of XML documents into account. Our parameterizable split algorithm dynamically maintains physical records of size smaller than a page which contain sets of connected tree nodes. This not only improves efficiency by clustering subtrees but also facilitates their compact representation. Existing approaches to store XML documents either use flat files or map every single tree node onto a separate physical record. The increased flexibility of our approach results in higher efficiency. Performance measurements validate this claim

Performance Evaluation of Structured and Unstructured Data in PIG/HADOOP and MONGO-DB Environments

The exponential development of data initially exhibited difficulties for prominent organizations, for example, Google, Yahoo, Amazon, Microsoft, Facebook, Twitter and so forth. The size of the information that needs to be handled by cloud applications is developing significantly quicker than storage capacity. This development requires new systems for managing and breaking down data. The term Big Data is used to address large volumes of unstructured (or semi-structured) and structured data that gets created from different applications, messages, weblogs, and online networking. Big Data is data whose size, variety and uncertainty require new supplementary models, procedures, algorithms, and research to manage and extract value and concealed learning from it. To process more information efficiently and skillfully, for analysis parallelism is utilized. To deal with the unstructured and semi-structured information NoSQL database has been presented. Hadoop better serves the Big Data analysis requirements. It is intended to scale up starting from a single server to a large cluster of machines, which has a high level of adaptation to internal failure. Many business and research institutes such as Facebook, Yahoo, Google, and so on had an expanding need to import, store, and analyze dynamic semi-structured data and its metadata. Also, significant development of semi-structured data inside expansive web-based organizations has prompted the formation of NoSQL data collections for flexible sorting and MapReduce for adaptable parallel analysis. They assessed, used and altered Hadoop, the most popular open source execution of MapReduce, for tending to the necessities of various valid analytics problems. These institutes are also utilizing MongoDB, and a report situated NoSQL store. In any case, there is a limited comprehension of the execution trade-offs of using these two innovations. This paper assesses the execution, versatility, and adaptation to an internal failure of utilizing MongoDB and Hadoop, towards the objective of recognizing the correct programming condition for logical data analytics and research. Lately, an expanding number of organizations have developed diverse, distinctive kinds of non-relational databases (such as MongoDB, Cassandra, Hypertable, HBase/ Hadoop, CouchDB and so on), generally referred to as NoSQL databases. The enormous amount of information generated requires an effective system to analyze the data in various scenarios, under various breaking points. In this paper, the objective is to find the break-even point of both Hadoop/Pig and MongoDB and develop a robust environment for data analytics

Anatomy of a Native XML Base Management System

Several alternatives to manage large XML document collections exist, ranging from file systems over relational or other database systems to specifically tailored XML repositories. In this paper we give a tour of Natix, a database management system designed from scratch for storing and processing XML data. Contrary to the common belief that management of XML data is just another application for traditional databases like relational systems, we illustrate how almost every component in a database system is affected in terms of adequacy and performance. We show how to design and optimize areas such as storage, transaction management comprising recovery and multi-user synchronisation as well as query processing for XML

Execution Performance Issues in Full-Text Information Retrieval

The task of an information retrieval system is to identify documents that will satisfy a user’s information need. Effective fulfillment of this task has long been an active area of research, leading to sophisticated retrieval models for representing information content in documents and queries and measuring similarity between the two. The maturity and proven effectiveness of these systems has resulted in demand for increased capacity, performance, scalability, and functionality, especially as information retrieval is integrated into more traditional database management environments. In this dissertation we explore a number of functionality and performance issues in information retrieval. First, we consider creation and modification of the document collection, concentrating on management of the inverted file index. An inverted file architecture based on a persistent object store is described and experimental results are presented for inverted file creation and modification. Our architecture provides performance that scales well with document collection size and the database features supported by the persistent object store provide many solutions to issues that arise during integration of information retrieval into vii more general database environments. We then turn to query evaluation speed and introduce a new optimization technique for statistical ranking retrieval systems that support structured queries. Experimental results from a variety of query sets show that execution time can be reduced by more than 50% with no noticeable impact on retrieval effectiveness, making these more complex retrieval models attractive alternatives for environments that demand high performance

An Efficient Database Storage Structure for Large Dynamic Objects

This paper presents storage structures and algorithms for the efficient manipulation of general-purpose large unstructured objects in a database system. The large object is stored in a sequence of variable-size segments, each of which consists of a large number of physically contiguous disk blocks. A tree structure indexes byte positions within the object. Disk space management is based on the binary buddy system. The scheme supports operations that replace, insert, delete bytes at arbitrary positions within the object, and append bytes at the end of the object. 1

Core Technologies for Native XML Database Management Systems

This work investigates the core technologies required to build Database Management Systems (DBMSs) for large collections of XML documents. We call such systems XML Base Management Systems (XBMSs). We identify requirements, and analyze how they can be met using a conventional DBMS. Our conclusion is that an XML support layer on top of an existing conventional DBMS does not address the requirements for XBMSs. Hence, we built a Native XBMS, called Natix. Natix has been developed completely from scratch, incorporating optimizations for high-performance XML processing in those places where they are most effective

Attribute-Level Versioning: A Relational Mechanism for Version Storage and Retrieval

Data analysts today have at their disposal a seemingly endless supply of data and repositories hence, datasets from which to draw. New datasets become available daily thus making the choice of which dataset to use difficult. Furthermore, traditional data analysis has been conducted using structured data repositories such as relational database management systems (RDBMS). These systems, by their nature and design, prohibit duplication for indexed collections forcing analysts to choose one value for each of the available attributes for an item in the collection. Often analysts discover two or more datasets with information about the same entity. When combining this data and transforming it into a form that is usable in an RDBMS, analysts are forced to deconflict the collisions and choose a single value for each duplicated attribute containing differing values. This deconfliction is the source of a considerable amount of guesswork and speculation on the part of the analyst in the absence of professional intuition. One must consider what is lost by discarding those alternative values. Are there relationships between the conflicting datasets that have meaning? Is each dataset presenting a different and valid view of the entity or are the alternate values erroneous? If so, which values are erroneous? Is there a historical significance of the variances? The analysis of modern datasets requires the use of specialized algorithms and storage and retrieval mechanisms to identify, deconflict, and assimilate variances of attributes for each entity encountered. These variances, or versions of attribute values, contribute meaning to the evolution and analysis of the entity and its relationship to other entities. A new, distinct storage and retrieval mechanism will enable analysts to efficiently store, analyze, and retrieve the attribute versions without unnecessary complexity or additional alterations of the original or derived dataset schemas. This paper presents technologies and innovations that assist data analysts in discovering meaning within their data and preserving all of the original data for every entity in the RDBMS