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The ADBIS conferences provide an international forum for the presentation of research on database theory, development of advanced DBMS technologies, and their applications. The 22nd edition of ADBIS, held on September 2–5, 2018, in Budapest, Hungary, includes six thematic workshops collecting contributions from various domains representing new trends in the broad research areas of databases and information systems.

We consider the information extraction framework known as document spanners, and study the problem of efficiently computing the results of the extraction from an input document, where the extraction task is described as a sequential variable-set automaton (VA). We pose this problem in the setting of enumeration algorithms, where we can first run a preprocessing phase and must then produce the results with a small delay between any two consecutive results. Our goal is to have an algorithm which is tractable in combined complexity, i.e., in the sizes of the input document and the VA; while ensuring the best possible data complexity bounds in the input document size, i.e., constant delay in the document size. Several recent works at PODS'18 proposed such algorithms but with linear delay in the document size or with an exponential dependency in size of the (generally nondeterministic) input VA. In particular, Florenzano et al. suggest that our desired runtime guarantees cannot be met for general sequential VAs. We refute this and show that, given a nondeterministic sequential VA and an input document, we can enumerate the mappings of the VA on the document with the following bounds: the preprocessing is linear in the document size and polynomial in the size of the VA, and the delay is independent of the document and polynomial in the size of the VA. The resulting algorithm thus achieves tractability in combined complexity and the best possible data complexity bounds. Moreover, it is rather easy to describe, in particular for the restricted case of so-called extended VAs. Finally, we evaluate our algorithm empirically using a prototype implementation., Comment: 29 pages. Extended version of arXiv:1807.09320. Integrates all corrections following reviewer feedback. Outside of some minor formatting differences and tweaks, this paper is the same as the paper to appear in the ACM TODS journal

Dependence logics are a modern family of logics of independence and dependence which mimic notions of database theory. In this paper, we aim to initiate the study of enumeration complexity in the field of dependence logics and thereby get a new point of view on enumerating answers of database queries. Consequently, as a first step, we investigate the problem of enumerating all satisfying teams of formulas from a given fragment of propositional dependence logic. We distinguish between restricting the team size by arbitrary functions and the parametrised version where the parameter is the team size. We show that a polynomial delay can be reached for polynomials and otherwise in the parametrised setting we reach FPT delay. However, the constructed enumeration algorithm with polynomial delay requires exponential space. We show that an incremental polynomial delay algorithm exists which uses polynomial space only. Negatively, we show that for the general problem without restricting the team size, an enumeration algorithm running in polynomial space cannot exist.

International audience; The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate ongoing expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.

The 21st century has been an era of information explosion. Huge volumes of spatial data have been created, collected than ever before. The constant stream of spatial data poses a great challenge for traditional data storage systems. This article reviews some fundamental concepts of spatial databases and database models for geographic information systems and introduces several types of databases that are employed for geospatial data, including traditional data storage solutions and the most popular NoSQL approaches for big data. And an overview of the history and development of spatial databases is represented to better explain the relevant technologies of spatial databases. Finally, some possible solutions for spatiotemporal big data are suggested.

When a relational database is queried, the result is normally a relation. Some queries, however, only require a yes/no answer; such queries are often called boolean queries. It is customary in database theory to express boolean queries by testing nonemptiness of query expressions. Another interesting way for expressing boolean queries are containment statements of the form Q 1 ⊆ Q 2 where Q 1 and Q 2 are query expressions. Here, for any input instance I, the boolean query result is true if Q 1 ( I ) is a subset of Q 2 ( I ) and false otherwise. In the present paper we will focus on nonemptiness and containment statements about conjunctive queries. The main goal is to investigate the monotone fragment of the containments of conjunctive queries. In particular, we show a preservation like result for this monotone fragment. That is, we show that, in expressive power, the monotone containments of conjunctive queries are exactly equal to conjunctive queries under nonemptiness.

Graph databases are a relatively new database technology gaining popularity. This article explores the value of graph databases and probes some of their security and privacy implications.

As a Computer scientist, a computer science students should have understanding about database theory as a concept of data maintenance. Database will be needed in every single human real life computer implementation such as information systems, information technology, internet, games, artificial intelligence, robot and so on. Inevitably, the right data handling and managament will produce excellent technology implementation. Data warehouse as one of the specialization subject which is offered in computer science study program final semester, provide challenge for computer science students.A survey was conducted on 18 students of early year of computer science study program at Surya university and giving hypothesis that for those students who ever heard of a data warehouse would be interested to learn data warehouse and on other hand, students who had never heard of the data warehouse will not be interested to learn data warehouse. Therefore, it is important that delivery of the Data warehouse subject material should be understood by lecturers, so that students can well understoodwith the data warehouse.

The paper examines the possibility of storing dynamic data in a relational database. Under dynamic data the paper refers to data with variable structure and variable nature of different periods in terms of research projects. The relational model of databases, which is used everywhere, presents each entity of the system in a separate table. Reasonable, relationships and dependencies between them are described by defining the relationship between the relational tables. Thus, changes in nomenclatures which would lead to changes in the databases of information system will cause the need for new fields in relational tables, or even new tables and relationships that will inevitably lead to changes in software solutions. The paper focuses on how we can reorganize the data in the system, so changes in the database due to changes in database structure to be minimal. The aim of this paper is to examine the way the data for research projects is stored within the database. Specific for the data is that it has a different structure for each calendar year. For this reason, a relational database should often change its structure in order to determine proper functioning of the system. The paper presents a method of storing data on the principle of attribute – value, where the software part cares for the integrity of the data and the database takes care only for the storage. The paper presents a model for such a dynamic storage in a relational database and examines its advantages and disadvantages.

This paper presents a database link network to measure the impact of databases on biological research. To this end, we used the 20,861 full-text articles from PubMed Central in the field of Bioinformatics. We then extracted databases from the methodology sections of these articles and their references. The list of databases was built with The 2013 Nucleic Acids Research Molecular Biology Database Collection (available online), which includes 1512 databases. The database link network was constructed from sets of pairs of databases mentioned in the methodology sections of full-text PubMed Central articles. The edges of the database link network represent the link relationships between two databases. The weight of each edge is determined either by the link frequency of the two databases (i.e., in the link-weighted database link network) or the topic similarity between two databases (i.e., in the similarity-weighted database link network). With the database link network, we analyzed the topological structure and main paths of the database link network to trace the usage, connection, and evolution of databases. We also conducted content analysis by comparing content similarities among the papers citing databases.

Traditional data integration delivers high integration quality but requires significant upfront effort because of the need for expensive experts to be involved. The pay-as-you-go approach to data integration aims to reduce this effort by relying on a bootstrap phase where algorithms replace experts in identifying or validating source-to-target semantic correspondences and executable mappings. Since the results of this phase are expected to be of lower quality, a continuous improvement phase is then launched where user feedback is collected and assimilated in order to improve the integration. It is crucial, therefore, to quantify integration quality. This paper presents a solution to this problem using feedback on mapping results as evidence. We contribute a methodology for quantifying integration quality while taking into account the inherent uncertainty of user feedback. The approach is evaluated in synthetic and real-world integration scenarios and shown to accurately and cost-effectively quantify their quality as a conditional probability.

Algorithms for the transaction rollback belong to the fundamentals of database theory and practice. Much less attention has been paid to the method rollback in the realm of object-oriented systems. This issue was studied in the context of exception handling. In a previous paper, a new algorithm for computing old attribute values has been proposed. In this paper that algorithm is used to implement a procedure for method rollback. It is shown that this procedure ensures the so called strong exception safety.

Database is nowadays a crucial data storage for application needs. Currently, there are several existing popular databases such as relational database like SQL and non-relational database (i.e. NoSQL). However, both types have their own drawbacks which could not be utilized in both online and offline modes concurrently. Therefore, the authors develop a database that supports both modes especially availability in offline mode without sacrificing consistency. The database is integrated with CRDT (conflict-free replicated data types) algorithm to handle and solve conflicts in synchronization process. Some tools and technologies are used in the database development including Apache Thrift for middleware, LevelDB for server database, and IndexedDB for temporary client database. The purpose of this development is to build a CRDT database with usable API for developer users. Finally, it is tested with scenarios to satisfy essential operations: add, remove, and update.

Traditional spatio-temporal data model (STDM) is based on relational database, it is hard to convert problem domain model to relational model, which result in complicated query and low expansibility. In this regard, we propose the spatio-temporal data model based on graph database. The data model integrates TGIS's three key elements: space, time and attributes, and expressed spatio-temporal characteristics of TGIS explicitly. Finally, this paper gives a particular description of logistics distribution route optimization. Experimental results show that the model is proved to be appropriate for expressing the spatio-temporal process of world.

In the last years, remarkable improvements have been made in the ability of distributed database systems performance. A distributed database is composed of some sites which are connected to each other through network connections. In this system, if good harmonization isn't made between different transactions, it may result in database incoherence. Nowadays, because of the complexity of many sites and their connection methods, it is difficult to extend different models in distributed database serially. The principle goal of concurrency control in distributed database is to ensure not interfering in accessibility of common database by different sites. Different concurrency control algorithms have been suggested to use in distributed database systems. In this paper, some available methods have been introduced and compared for concurrency control in distributed database.

Treating communication as information exchange between systems, we employ the most fundamental structure in mathematics, nature and cognition, which is called a named set or a fundamental triad because it has been useful in a variety of areas such as networks and networking, physics, information theory, mathematics, logic, database theory and practice, artificial intelligence, mathematical linguistics, epistemology and methodology of science, to mention but a few. Here we use structural models based on the theory of named sets for description and analysis of interpersonal communication explicating its structural regularities.

In this paper we propose a new internationalization model for databases. This model is intended to enable the internationalization (i18n) in a database, thus facilitating the work of both the database developer as well as the translator which performs its localization (l10n). The use of this model should reduce the developer's effort and allow the translator not to worry about application implementation details. On the other hand, with this model it will be possible to do l10n maintenance keeping the database online. We have implemented an internal module for the database engine that allows the detachment of all the elements dependent on the country language and culture being used. This detachment makes the elements' source independent enough so that its components can be edited by a translator without any databases knowledge and refilled at run time, i.e. with the database in full operation. This also allows new sources to be added for different cultures and languages. The proposed model may be useful for several types of applications, an interesting example would be in the development of business applications where much of its rules and validations that defines the company's business is applied at the database level. With this solution for database internationalization it is not necessary to create specific tables, relations and any other needed database objects, which requires more expensive query and maintenance methods.

As most database users cannot precisely express their information needs in the form of database queries, it is challenging for database query interfaces to understand and satisfy their intents. Database systems usually improve their understanding of users' intents by collecting their feedback on the answers to the users' imprecise and ill-specified queries. Users may also learn to express their queries precisely during their interactions with the database system. In this paper, we report our progress on developing a formal framework for representing and understanding information needs in database querying and exploration. Our framework considers querying as a collaboration between the user and the database system to establish a mutual language for representing information needs. We formalize this collaboration as a signaling game between two potentially rational agents: the user and the database system. We believe that this framework naturally models the long-term interaction of users and database systems.

Recent works on bounding the output size of a conjunctive query with functional dependencies and degree constraints have shown a deep connection between fundamental questions in information theory and database theory. We prove analogous output bounds for disjunctive datalog rules, and answer several open questions regarding the tightness and looseness of these bounds along the way. Our bounds are intimately related to Shannon-type information inequalities. We devise the notion of a "proof sequence" of a specific class of Shannon-type information inequalities called "Shannon flow inequalities". We then show how such a proof sequence can be interpreted as symbolic instructions guiding an algorithm called "PANDA", which answers disjunctive datalog rules within the time that the size bound predicted. We show that PANDA can be used as a black-box to devise algorithms matching precisely the fractional hypertree width and the submodular width runtimes for aggregate and conjunctive queries with functional dependencies and degree constraints. Our results improve upon known results in three ways. First, our bounds and algorithms are for the much more general class of disjunctive datalog rules, of which conjunctive queries are a special case. Second, the runtime of PANDA matches precisely the submodular width bound, while the previous algorithm by Marx has a runtime that is polynomial in this bound. Third, our bounds and algorithms work for queries with input cardinality bounds, functional dependencies, and degree constraints. Overall, our results show a deep connection between three seemingly unrelated lines of research; and, our results on proof sequences for Shannon flow inequalities might be of independent interest.

Parallel join algorithms have received much attention in recent years, due to the rapid development of massively parallel systems such as MapReduce and Spark. In the database theory community, most efforts have been focused on studying worst-optimal algorithms. However, the worst-case optimality of these join algorithms relies on the hard instances having very large output sizes. In the case of a two-relation join, the hard instance is just a Cartesian product, with an output size that is quadratic in the input size.In practice, however, the output size is usually much smaller. One recent parallel join algorithm by Beame et al.[8] has achieved output-optimality, i.e., its cost is optimal in terms of both the input size and the output size, but their algorithm only works for a 2-relation equi-join, and has some imperfections. In this paper, we first improve their algorithm to true optimality. Then we design output-optimal algorithms for a large class of similarity joins. Finally, we present a lower bound, which essentially eliminates the possibility of having output-optimal algorithms for any join on more than two relations.

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search., Comment: 20 pages, to appear to IEEE Security and Privacy

Rapid and vast growth of data volume triggers a need for data management system with good scalability, availability and reliability. NoSQL database comes as a database management system developed to answer the need for such large scale and distributed data management. It is considered more effective and efficient compared to relational database. This database characteristics are very different than relational database. Therefore, a good method is necessary in order to transform data from relational database to NoSQL database. The Graph Transforming Algorithm is one of the simple methods and easy to use for that purpose. However, the result of conversion using this method remains with a considerably high level of data redundancy, making it necessary to further develop the algorithm. This research proposes a new algorithm called Enhanced Graph Transforming Algorithm, a development of Graph Transforming Algorithm by adding rules to eliminate transitive dependency between vertices prior to joining the vertices. Based on the test conducted in the research, the Enhanced Graph Transforming Algorithm is proven capable of reducing the number of redundant attributes and suppressing waste of space on data storage by up to 39.68% lower than the result of transformation generated using Graph Transforming Algorithm.

The database management system is a software that enables easier work with databases i.e. to define database structure, retrieve stored data, enter data into the database and process the previously stored data in the database. In this article we have compared 3 relational database management systems (RDBMS) - Oracle 11g, MySQL and PostgreSQL. They are compared according to the simple criteria that we defined, such as the comparison of basic data, syntax, data types and speed performance. The main contribution of the article is a comparison of 3 different RDBMSs by our own score criteria.

The world nowadays revolves around dealing with extreme large amount of data presented in various formats. So it is inevitable that researchers focus on advancing the state of managing information. From here, the importance of database technology ranks amongst the hottest areas of research, taking into account the consistent need for faster query processing as well as for managing huge amounts of data. This year the track has received many papers covering different areas of databases.

In the verifiable database (VDB) model, a computationally weak client (database owner) delegates his database management to a database service provider on the cloud, which is considered untrusted third party, while users can query the data and verify the integrity of query results. Since the process can be computationally costly and has a limited support for sophisticated query types such as aggregated queries, we propose in this paper a framework that helps bridge the gap between security and practicality trade-offs. The proposed framework remodels the verifiable database problem using Stackelberg security game. In the new model, the database owner creates and uploads to the database service provider the database and its authentication structure (AS). Next, the game is played between the defender (verifier), who is a trusted party to the database owner and runs scheduled randomized verifications using Stackelberg mixed strategy, and the database service provider. The idea is to randomize the verification schedule in an optimized way that grants the optimal payoff for the verifier while making it extremely hard for the database service provider or any attacker to figure out which part of the database is being verified next. We have implemented and compared the proposed model performance with a uniform randomization model. Simulation results show that the proposed model outperforms the uniform randomization model. Furthermore, we have evaluated the efficiency of the proposed model against different cost metrics.

Popularity of bigdata in cloud computing is growing tremendously nowadays. Data production rate has become explosive. Many existing systems need to enlarge their service to sustenance explosive data generation rate. NoSQL database are suitable to handle large volume of data. This paper focuses on a data adapter which provides support for hybrid database systems, NoSQL as well as relational databases. Also, a seamless mechanism is provided for hybrid database system construction, which enables access to both databases at same time. This paper focuses on the automatic transformation of hybrid systems with multiple structured data. Hybrid database systems provide access to either relational or NoSQL database, per data size. The proposed data adapter supports application queries and database transformation at same time, which in turn speed up the process. Three different approaches are proposed for queries on database. Blocking transformation mode, blocking truncation mode, direct access mode. This paper also discusses the integration of Pentaho business analytics tool with proposed data adapter for big data predictive analytics. This integration focuses on empowering users to prepare, model and visualize and explore data sets stored in NoSQL databases. This paper describes the design of data adapter in detail.

Recently the No-SQL databases has been popularly used in the mobile and web applications. There are several types of No-SQL databases such as columnar, key-value, graph databases and finally the document store database which is efficient and has more dynamic queries than the normal RDBMS. This paper proposes an automatic method to map the data from the relational database to document store database (MONGODB). This is true for both structured and unstructured data such as word document files. The proposed method also has the capability of extracting the keywords from Blobs “Binary Large Object” Stored in relational databases to be mapped inside the MONGODB. The results show a complexity of order n where n is the number of records processed when it comes to the performance in creating the output from mapping the relational database to document store database for both types of data.

Databases are imperative for research in bioinformatics and computational biology. Current challenges in database design include data heterogeneity and context-dependent interconnections between data entities. These challenges drove the development of unified data interfaces and specialized databases. The curation of specialized databases is an ever-growing challenge due to the introduction of new data sources and the emergence of new relational connections between established datasets. Here, an open-source framework for the curation of specialized databases is proposed. The framework supports user-designed models of data encapsulation, objects persistency and structured interfaces to local and external data sources such as MalaCards, Biomodels and the National Centre for Biotechnology Information (NCBI) databases. The proposed framework was implemented using Java as the development environment, EclipseLink as the data persistency agent and Apache Derby as the database manager. Syntactic analysis was based on J3D, jsoup, Apache Commons and w3c.dom open libraries. Finally, a construction of a specialized database for aneurysms associated vascular diseases is demonstrated. This database contains 3-dimensional geometries of aneurysms, patient’s clinical information, articles, biological models, related diseases and our recently published model of aneurysms’ risk of rapture. Framework is available in: http://nbel-lab.com. Electronic supplementary material The online version of this article (doi:10.1186/s13040-017-0130-z) contains supplementary material, which is available to authorized users.

Industries use information for decision making to help process changers and business expansions. They use historical data to produce the required information. Preparation of information varies with the way of arranging, retrieving and processing data accurately. Decision support systems use many different approaches to design and data manipulation. Existing small to medium systems still use relational databases and basic query models as primary data analysis technique. But large scaled enterprise systems use different analytical, data warehousing and business intelligent systems rather than using databases due to the numerous RDBMS limitations. As described in (Ballard, et al., 1998, February) data warehousing concept evolve the easy access method of quality data in a structured store for decision making. Objective of this paper is to introduce an advanced database and query model design to support data analysis, business intelligence and data visualization over relational database management systems.

Nowadays, Demands of web scale are in increasing and growing rapidly. Mobile applications, web technologies, social media always generates unstructured data that had lead to the advent of various NoSQL databases. Therefore, Big data applications are necessary to have an efficient technology to collect these data. However, a relational database is the traditional database that always uses in many applications and still has more valuable to play a significant role in the current information system. The main characteristics of NoSQL databases are schema-free, no relationship, no need to join as a relational database. The business organization expects that NoSQL database has better performance than a relational database. In This paper, we aim to compare the performance of Redis, which is a key-value database, one kind of NoSQL database, and MariaDB, which is a popular relational database. We designed a set of experiments with a large amount of data and compared the efficiency of the insert, update, delete and select transactions from various aspects on the same dataset. We measure the processing time of each transaction to evaluate the comparison. The results have shown that Redis has better runtime performance for insert, delete, update transaction under a specific condition or complex queries. MariaDB still is good for some conditions especially when we have a small data. Our study can help to choose a database that will be suitable for the real world applications because relational databases and NoSQL databases have different strengths and weakness.

The paper considers the stage of preparing the database for multi-factor analysis by means of the algebraic model of constructive logic that has been used successfully since 1999 to perform the analysis in medicine and biology. Initial data for model building is a table. Each line in this table is treated as a case where the values of factors and their impacts are marked. The resulting model is represented by a set of result components in the form of factors indicating the limit of detection combined by conjunction sing (pointing to the combined effect). Each resulting component is characterized by the capacity is the essence of the number of lines in the table, which correspond to the specified limits of determining factors in their joint action. The resulting logical expression is characterized by a combination of factors (indicating the detection limits of each of them) in their capacity as the degree of influence on the result. The initial table data should not have contradictions (when the aim is achieved and isn´t achieved by the same values of the factors). To this aim, the program envisages the exception of those targeted lines of which coincide with non-target strings. However, this isn´t always acceptable in cases of a large number of matching target lines and the singular numbers of non-target strings. Then a large number of cases due to the single non-target line are excluded. The analysis of the coincidences of target and non-target lines to select a single non-target line, to remove them from the database on the example of connective tissue dysplasia with magnesium therapy has been proposed. Comparative analysis of the obtained mathematical models was carried out. The effect of improvement of mathematical model on the basis of algebraic model of constructive logic was demonstrated.

The paper starts from performance optimization of Oracle database. Based on analysis of Oracle structure, the paper explores performance optimization of Oracle database for database parameters, application system and network load. And the paper expounds reasonable planning of database and how to analyze and improve the performance after operation, and how to optimize a database with satisfying performance.

In order to make up the real-time performance of tracking and control information database, this paper design a kind of two-layer’s real-time data storage model based on memory database and relational database. In this article, the two-layer’s real-time data storage mechanism and life cycle are expounded in detail, analyzing and inducing the real-time data characteristic and storage strategy, putting forward the memory database’s self-adaptive index algorithm of T-tree index and hash index, and introducing the database synchronization mechanism between the memory database and relational database and so on. In this way, so as to improve and optimize the real-time, reliability and security of database, provides a reliable data guarantee for future expansion of the real-time application.

With the rapid development of Internet technology, the management capacity of traditional relational databases becomes relatively inefficient when facing the access and processing of big data. As a kind of non-relational databases, the key-value stores, with its high scalability, provide an efficient solution to the problem. This article introduces the concept and features of Key-Value stores, and followed by the comparison with the traditional relational databases, and an example is illustrated to explain its typical application and finally the existing problems of Key-Value stores are summarized.

How to coordinate heterogeneous database to realize sharing and consistency of information not only is an important research subject in the database filed, but also is an emerging research hot spot in the field of database system. As a new development direction of computer software, it applies some new techniques to integrate the existing systems to develop new applications. The paper analyzes CSCW, XML, JDBC and heterogeneous database, and proposes a solution of heterogeneous database. The solution absorbs the thoughts of database meta data, middle ware and LDAP directory service.

In this paper we thoroughly cover the issue of blank nodes, which have been defined in RDF as "existential variables". We first introduce the theoretical precedent for existential blank nodes from first order logic and incomplete Information in database theory. We then cover the different (and sometimes incompatible) treatment of blank nodes across the W3C stack of RDF-related standards. We present an empirical survey of the blank nodes present in a large sample of RDF data published on the Web (the BTC-2012 dataset), where we find that 25.7% of unique RDF terms are blank nodes, that 44.9% of documents and 66.2% of domains featured use of at least one blank node, and that aside from one Linked Data domain whose RDF data contains many "blank node cycles", the vast majority of blank nodes form tree structures that are efficient to compute simple entailment over. With respect to the RDF-merge of the full data, we show that 6.1% of blank-nodes are redundant under simple entailment. The vast majority of non-lean cases are isomorphisms resulting from multiple blank nodes with no discriminating information being given within an RDF document or documents being duplicated in multiple Web locations. Although simple entailment is NP-complete and leanness-checking is coNP-complete, in computing this latter result, we demonstrate that in practice, real-world RDF graphs are sufficiently "rich" in ground information for problematic cases to be avoided by non-naive algorithms.

Testing is essential for quality assurance of database applications. Achieving high code coverage of the database applications is important in testing. In practice, there may exist a copy of live databases that can be used for database application testing. Using an existing database state is desirable since it tends to be representative of real-world objects' characteristics, helping detect faults that could cause failures in real-world settings. However, to cover a specific program-code portion (e.g., block), appropriate program inputs also need to be generated for the given existing database state. To address this issue, in this paper, we propose a novel approach that generates program inputs for achieving high code coverage of a database application, given an existing database state. Our approach uses symbolic execution to track how program inputs are transformed before appearing in the executed SQL queries and how the constraints on query results affect the application's execution. One significant challenge in our problem context is the gap between program-input constraints derived from the program and from the given existing database state; satisfying both types of constraints is needed to cover a specific program-code portion. Our approach includes novel query formulation to bridge this gap. We incorporate the data-instantiation component in our framework to deal with the case that no effective program input values can be attained. We determine how to generate new records and populate them in the new database state such that the code along the path can be covered. We also extend our approach of program-input generation to test database applications including canonical queries and group-by queries. Our approach is loosely integrated into Pex, a state-of-the-art white-box testing tool for .NET from Microsoft Research. Empirical evaluations on two real database applications show that our approach assists Pex to generate program inputs that achieve higher code coverage than the program inputs generated by Pex without our approach's assistance.

This paper analyzes the functional design and structure design of the database security model networks, research and further realizes the key features of the model. The system is suitable for network security database management system, which has a certain universality and portability.

The design of data mining system in database is researched. Vast amounts of information contained in the database, and the data show the diversity of characteristics, resulting in lower efficiency of data mining in database, which database brought greater difficulties to information query. To avoid these shortcomings, database performance optimization method based on cloud computing is proposed. The model of cloud computing data relationship is established to describe the connection between related data inthe database, thus providing the basis for data query. The load state of data nodes is calculated to enable rapid information inquiryin the database. Experimental results show that using this algorithm to optimize data inquiry in database can improve the efficiency of informationinquiry indatabase effectively.

In this paper we explore the problem of counting solutions to conjunctive queries. We consider a parameter called the \emph{quantified star size} of a formula $\varphi$ which measures how the free variables are spread in $\varphi$. We show that for conjunctive queries that admit nice decomposition properties (such as being of bounded treewidth or generalized hypertree width) bounded quantified star size exactly characterizes the classes of queries for which counting the number of solutions is tractable. This also allows us to fully characterize the conjunctive queries for which counting the solutions is tractable in the case of bounded arity. To illustrate the applicability of our results, we also show that computing the quantified star size of a formula is possible in time $n^{O(k)}$ for queries of generalized hypertree width $k$. Furthermore, quantified star size is even fixed parameter tractable parameterized by some other width measures, while it is $\W{1}$-hard for generalized hypertree width and thus unlikely to be fixed parameter tractable. We finally show how to compute an approximation of quantified star size in polynomial time where the approximation ratio depends on the width of the input., Comment: 29 pages, 3 figures Preliminary version appeared in ICDT'2013

There are two obvious ways to map a two-dimension relational database table onto a one-dimensional storage interface: store the table row-by-row, or store the table column-by-column. Historically, database system implementations and research have focused on the row-by row data layout, since it performs best on the most common application for database systems: business transactional data processing. However, there are a set of emerging applications for database systems for which the row-by-row layout performs poorly. These applications are more analytical in nature, whose goal is to read through the data to gain new insight and use it to drive decision making and planning. In this paper, we study about the facts responsible for making Column Oriented database when traditional Row-oriented databases are already present, analysis of generation of Column Oriented databases, etc. Finally It will be conclude by giving a probabilistic analysis of Column Oriented database. Keywords-Data warehouses, Database systems, Databases, Performance analysis

In order to solve the problem of data integration and sharing for heterogeneous database at various departments from enterprises, this paper analyses thoroughly how to use the XML technology to mask differences between the heterogeneous databases. This paper designs an integration model based on the various heterogeneous databases by using the XML database as the middle database. The integration model is applied in the fault diagnosis system, and a data-exchange platform based on BizTalk is designed. On the heterogeneous database model, the fault diagnosis system can meets the need of multiple heterogeneous databases.

Database technology is one of the fastest growing areas of computer science, is also one of the most widely used technology, it has become the core of the computer information system and application system technology and the important basis. In this paper, we discuss all the important aspects of database design process, including requirements analysis phase; The conceptual design stage; The logical design phase; The physical design phase; Database implementation stage; Database operation maintenance phase six stages, and puts forward various problems occurred in the database design, and a variety of ways to solve these problems were analyzed.

In order to gain high response speed, the paper presents a hybrid Timesten and Oracle database structure. First, the paper creates concepts of real-time database and historical database. It also points out their relationships. Second, it builds a hardware structure which involves an application server, a Timesten database server and an Oracle database server. The structure adopts uninterruptible power supply for data reliability. Last, it presents a software structure of databases. The Timesten acts as the real-time database and the Oracle acts as the historical database. There is a layer in front of the Timesten for data acquiring and an agent layer between the Timesten and the Oracle for data transmission. Experiment shows that the Timesten is about 6.69 times faster than the Oracle disk database.

Database systems are widely used in many fields. As the scale of database systems increases, more firms decide to deploy the database system in a distributed environment for the sake of data integrity fault tolerance. For guaranteeing the consistency of data, database management systems adopt isolation policies. This paper discusses probabilistic models for snapshot isolation of database management system. Snapshot isolation is an effective method to enhance the consistency of database system, although it degrades the system performance where the system has large network latency such as distributed database system. Also, under the failure-prone environment, a restart scheme is considered as one countermeasure. This paper proposes probabilistic models for the dynamics of snapshot isolation of database system and exhibits the optimization of system performance with respect to updating interval of snapshot isolation within the failure-prone environment from the analytical point of view. Numerical experiments are conducted to validate the effectiveness of analytical results by using real traffic data.

To evaluate the performance of database applications and database management systems (DBMSs), we usually execute workloads of queries on generated databases of different sizes and then benchmark various measures such as respond time and throughput. This paper introduces MyBenchmark, a parallel data generation tool that takes a set of queries as input and generates database instances. Users of MyBenchmark can control the characteristics of the generated data as well as the characteristics of the resulting workload. Applications of MyBenchmark include DBMS testing, database application testing, and application-driven benchmarking. In this paper, we present the architecture and the implementation algorithms of MyBenchmark. Experimental results show that MyBenchmark is able to generate workload-aware databases for a variety of workloads including query workloads extracted from TPC-C, TPC-E, TPC-H, and TPC-W benchmarks.

Testing database applications typically requires the generation of tests consisting of both program inputs and database states. Recently, a testing technique called Dynamic Symbolic Execution (DSE) has been proposed to reduce manual effort in test generation for software applications. However, applying DSE to generate tests for database applications faces various technical challenges. For example, the database application under test needs to physically connect to the associated database, which may not be available for various reasons. The program inputs whose values are used to form the executed queries are not treated symbolically, posing difficulties for generating valid database states or appropriate database states for achieving high coverage of query-result-manipulation code. To address these challenges, in this article, we propose an approach called SynDB that synthesizes new database interactions to replace the original ones from the database application under test. In this way, we bridge various constraints within a database application: query-construction constraints, query constraints, database schema constraints, and query-result-manipulation constraints. We then apply a state-of-the-art DSE engine called Pex for .NET from Microsoft Research to generate both program inputs and database states. The evaluation results show that tests generated by our approach can achieve higher code coverage than existing test generation approaches for database applications.

To meet the test requirements of a certain kind missile, the general test system based on the PXI bus and test adapters is designed, The generality of the system is achieved by designing corresponding adapter for different types of missiles, the framework based on the function interface and the method of the test tasks handle dynamic management are used in the software, the expert systemthe database theory and other theories are used in the intelligence faults diagnosis system. The practical application results indicated that the system has many advantages such as better generality, high automatization and more convenience.

In this paper we introduce cross tree and block mathematical principles into the design of database system, divide the time sequence and storage space of computer database system, establish the mathematical model and algorithm of computer resources database system, and design the test database system. In this paper, we use high performance interface of Display Port, by way of coupling to communicate on two port control, and use RHEL 6.2 Linux virtual machine to do simulation experiment on process of database system. Through the simulation we find the API which is called by Read, Close, Mmap, Stat, Fstat is similar. It is consistent with the actual situation, and verifies the reliability of the program. Finally, we apply the database system to the network database construction of sports literature resources in the new town of Poyang Lake area. It reaches the effect that sport resources are shared by all. It provides technical support for the application of computer database system.