HERMES

Integration of large heterogeneous data collections, gathered usually for making decisions on information security (IS) management issues, requires a preliminary step -- unification of their data models. It is provided by mapping the source data models into the canonical information model. Information and semantics of data definition languages (DDL) and semantics of the operations of data manipulation languages (DML) have to be preserved by the mapping. This research is devoted to the unification of the graph data models (GDM) -- the important kind of the existing various data models. The distinguishing features of modern GDM are discussed as well as their application in the information security (IS) area. The issues of proof of DDL and DML semantics preserving by the mapping of the GDM into the object-frame canonical model are briefly considered. Future work steps in applying research results to different IS management areas are indicated in conclusion.

Data mining is the discovery of knowledge and useful information from the large amounts of data stored in databases. The emerging data mining tools and systems lead to the demand of a powerful data mining query language. The concepts of such a language for relational databases are discussed before. With the increasing popularity of object-oriented databases, it is important to design a data mining query language for such databases. The main objective of this paper is to propose an Object Data Mining Query Language (ODMQL) for object-oriented databases as an extension to the Object Query Language (OQL) proposed by the Object Data Management Group (ODMG) as a standard query language for object-oriented databases. The proposed language is implemented as a feature of an experimental object-oriented database management system that is developed as a testbed for research issues of object-oriented databases.

CHAPTER 15 - INFORMATION RESOURCE MANAGEMENT

Translating object-oriented database transactions into relational transactions

: The purpose of IDA Memorandum Report M-459, An Oracle - Ada/SQL implementation, is to identify and describe a version of a software delivery, an Oracle - Ada/SQL (Structured Query Language) implementation, to the WIS Joint Program Management Office. The software system provides a Level 1 Ada/SQL implementation which interacts with the Oracle database management system (DBMS) . It provides the ability to define Ada/SQL Data Definition Language (DDL) and Ada/SQL Data Manipulation Language (DML) which will be converted to the appropriate SQL calls required by the Oracle DBMS. Ada/SQL is a binding between Ada and the database programming language SQL. The software was developed on a VAX 8600, using VAX/VMS version 4.6, and the DEC Ada compiler, version 1.4-33.

Proposes a query language, called MQL (Multimedia Query Language), for specifying and manipulating various types of multimedia information efficiently. The data models that describe the multimedia applications and varieties of media data types are established with the object-oriented notation. What the query language consists of can be separated into two parts: one is the data definition language and the other is the data manipulation language. In addition to common functions, MQL provides additional features, such as historical alternative multimedia matching queries, complex queries and nested queries. These functions satisfy most of the requirements of multimedia database querying. Consequently, MQL offers more powerful data access mechanisms for multimedia applications.

The developments of Information and Communication Technology (ICT) and Internet of Things (IoT) are being used to enhance quality, performance and interactivity of urban services. The developments of Information and Communication Technology (ICT) and Internet of Things (IoT) are being used to enhance quality, performance and interactivity of urban services. Benefited from the widespread adoption of mobile devices, we can collect amount of mobile data for user mobility analysis. Mining hidden information from users' mobile data is important for builders of smart city to provide better location-based service. This paper focuses on two classical domain-independent prediction models and one improved Markov model that are capable of estimating the next location. By using 27-day-long traffic data of mobile network, we extract trajectories of 4914 individuals for experiments. We find that the original Markov algorithm has a better performance in resource consumption than LZ family algorithms, but its prediction accuracy is lower than prediction accuracy of LeZi Update and Active LeZi algorithm. In order to improve the prediction accuracy of Markov and overcome drawbacks of traditional prediction algorithms, we present a new method based on Markov, which considers both temporal and spatial factors. Extensive experiments demonstrate our improved method has a better performance in location prediction. In addition, we further study the relationship between prediction accuracy and trajectory's regularity, to identify the most suitable prediction algorithm for a trajectory.

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

In large-scale wireless sensor networks the data- gathering mechanism to introducing mobility into the network. . We consider the location service in a WSN, where each sensor needs to maintain its location information by 1) frequently updating its location information within its neighboring set of polling points in the network. In the previous work a mobile data collector, for convenience called an M-collector for gathering information from the sensor and send to sink node. M- collector could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, working like a mobile base station and gathering data while moving through the field. An M-collector starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range, then directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. Since data packets are directly gathered without relays and collisions, the lifetime of sensors is expected to be prolonged. In this paper, we mainly focus on the problem of minimizing the length of each data-gathering tour and remove the redundancy for data gathering. We develop a stochastic sequential decision framework to analyze this problem. Under a Markovian mobility model, the location update decision problem is modeled as a Markov Decision Process (MDP). For the work with strict distance/ time and energy constraints, we consider utilizing single and multiple M-collectors and propose a data- gathering algorithm where multiple M-collectors traverse through several shorter subtours concurrently to satisfy the distance/time and energy constraints. To identify the redundancy of data from the sensor using Pearson auto correlated technique. Our proposed system for mobile data gathering scheme can improve the scalability and balance the energy consumption among sensors.

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

In the previous chapters, we introduced several dimensions that are useful to describe and measure information quality in its different aspects and meanings. Focusing on structured data, database management systems (DBMSs) represent data and relative operations on it in terms of a data model and a data definition and manipulation language, i.e., a set of structures and commands that can be represented, interpreted, and executed by a computer. We can follow the same process to represent, besides data, their quality dimensions. This means that in order to represent data quality, we have to extend data models.

Databases are well suited to the task of describing and organizing research datasets, however, the difficulties of using database management systems effectively have resulted in their limited usage among domain scientists. Scientists operate in an environment that is changing steadily with new experimental protocols, instruments, and discoveries that impact what datasets they generate and how they describe and organize them. In order to manage datasets for a scientific application, scientists need to routinely revise their database schemas to reflect these changes. Unfortunately, evolving a database is one of the well-known and most difficult aspects of database usage. The conventional data definition and manipulation languages offer relatively low-level programming abstractions to perform complex database evolution tasks, and therefore require specialized technical skills not possessed by most domain scientists. A simplified means of expressing database evolution operations can reduce the effort of keeping the scientific database in sync with changing requirements. This paper presents a high-level, user-oriented, schema evolution framework with an algebra of specialized schema modification operators. The approach allows introduction of novel operators as motivated by new requirements and is amenable to well established optimization techniques for efficient planning and execution. We present the framework and its implementation, and we demonstrate its utility in an exemplar use case and performance evaluation.

Query languages for graph databases started to be investigated some 25 years ago. With much current data, such as linked data on the Web and social network data, being graph-structured, there has been a recent resurgence in interest in graph query languages. We provide a brief survey of many of the graph query languages that have been proposed, focussing on the core functionality provided in these languages. We also consider issues such as expressive power and the computational complexity of query evaluation.

Graph analytics is becoming increasingly popular, driving many important business applications from social network analysis to machine learning. Since most graph data is collected in a relational database, it seems natural to attempt to perform graph analytics within the relational environment. However, SQL, the query language for relational databases, makes it difficult to express graph analytics operations. This is because SQL requires programmers to think in terms of tables and joins, rather than the more natural representation of graphs as collections of nodes and edges. As a result, even relatively simple graph operations can require very complex SQL queries. In this paper, we present GRAPHiQL, an intuitive query language for graph analytics, which allows developers to reason in terms of nodes and edges. GRAPHiQL provides key graph constructs such as looping, recursion, and neighborhood operations. At runtime, GRAPHiQL compiles graph programs into efficient SQL queries that can run on any relational database. We demonstrate the applicability of GRAPHiQL on several applications and compare the performance of GRAPHiQL queries with those of Apache Giraph (a popular ‘vertex centric’ graph programming language).

For multimedia databases, there are not only different media types, but also different ways to query the databases. The query mechanisms may include free text search, SQL-like querying, icon-based techniques, querying based upon the entity-relationship (ER) diagram, content-based querying, sound-based querying, as well as virtual-reality (VR) techniques. We will first discuss query languages for multimedia databases with emphasis on visual query languages, and then survey recent advances in query languages for heterogeneous multimedia databases and data sources. A powerful spatial/temporal query language called ΣQL is described, and we will illustrate this approach by data fusion examples.