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Query formalisms and facilities have received significant attention in the past decades resulting in the development of query languages with varying characteristics; many of them resemble sql. Query facilities typically ship as part of database management systems or, sometimes, bundled with programming languages. For applications written in imperative programming languages, database management systems impose an expensive model transformation. In-memory data structures can represent sophisticated relationships in a manner that is efficient in terms of storage and processing overhead, but most general purpose programming languages lack an interpreter and/or an expressive query language for manipulating interactive queries. Issuing interactive ad-hoc queries on program data structures is tough. This work presents a method and an implementation for representing an application's arbitrary imperative programming data model as a queryable relational one. The Pico COllections Query Library (pico ql) uses a domain specific language to define a relational representation of application data structures and an sql interface implementation. Queries are issued interactively and are type safe. We demonstrate our relational representation for objects and the library's usefulness on three large c++ projects. pico ql enhances query expressiveness and boosts productivity compared to querying via traditional programming constructs.

Database management systems (DBMS) have been widely used for storing and retrieving data. However, databases are often hard to use since their interface is quite rigid in cooperating with users. End user is required to issue SQL query to retrieve information from the database. Thus, a user cannot interact directly because of lack of knowledge about the SQL query form, thus is restricted to available application options. To provide the direct access of user on dataset, some unstructured query processor is required. The present work is an interface that will convert the unstructured text query to structured SQL query. It is an intelligent interface formation between the user and the dataset and defined in three main stages namely Descriptive Database Construction, Unstructured Query Filtration and Mapping Stage. The system provide the robustness in terms to handle the broader range of user queries and is be implemented in java environment on Enterprise Employee Database. General Terms Natural Language Processing, Natural Language Interface to Database.

While SQL has a relatively simple syntax with a small set of commands, it allows for complex query construction that is deceptively challenging. SQL is often the first declarative programming language learned by students, which requires a different analysis approach than that used to understand the procedural or object-oriented programming paradigms with which students are more familiar. While some tools exist to help assess the correctness of an SQL query or address syntax errors, few tools focus on query semantics. DBQA does so by illustrating the effects that clauses and conditions have on an SQL SELECT statement using a visualized, data-oriented approach. We expect DBQA's approach to query evaluation to feel more familiar to those with experience in procedural or object-oriented programming and to help those users better understand the semantics of complex queries. DBQA includes support for SQL queries containing JOINs and subqueries, which have been shown to be among the most difficult for students to construct.

Data analysis and visualizations techniques (such as split-apply-combine) make extensive use of associative tabular data-structures that are cumbersome to use with common aggregation APIs (for arrays, lists or dictionaries). In these cases a fluent API for querying associative tabular data (like the ones provided by Pandas, Mathematica or LINQ) is more appropriate for interactive exploration environments. In Smalltalk despite the fact that many important analysis tools are already present (for e.g., in the PolyMath library), we are still missing this essential part of the data science toolkit. These specialized data structures for tabular datasets can provide us with a simple and powerful API for summarizing, cleaning, and manipulating a wealth of data-sources that are currently cumbersome to use. In this paper we introduce the DataFrame and DataSeries collections - that are specifically designed for working with structured data. We demonstrate how these tools can be used for descriptive statistics and Exploratory Data Analysis (EDA) - the critical first step of data analysis which allows us to get the summary of a dataset, detect mistakes, determine the relations, and select the appropriate model for further confirmatory analysis. We then detail the implementation trade-offs that we are currently facing in our implementation for Pharo and discuss future perspectives.

Bringing computations close to the data source is one of the key concepts of high-performance database management systems. This specifically holds true for state-of-the-art systems like HyPer, Microsoft SQL Server, or SAP HANA that are making efficient use of modern hardware by exploiting in-memory storage, vector instructions, and sophisticated algorithms for query optimization and execution. To benefit from the capabilities of these systems, it becomes increasingly important for business applications to efficiently push their data-intensive parts to the DBMS. Capturing and communicating the computational intent from the application to the DBMS is often not trivial, as the DBMS is either abstracted away using object-relational mapper interfaces like Hibernate, or as the language mismatch between imperative, object-oriented languages like Java and the declarative, SQL-based query processing makes it difficult for developers to express non-trivial database operations. In this presentation, we discuss how some of these issues can be tackled using the Core Data Services (CDS) framework that is both supported by SAP's HANA data-base and the Netweaver application server stack. By combining concepts from declarative query languages like SQL and XPath with functionality developers are familiar with from object-oriented languages or even aspect-oriented programming (i.e. the "."-operator for member access and the concept of annotations), CDS enables to express complex business logic with only a few lines of code - where equivalent SQL statements span multiple pages instead (see Figure 1 below for an example).

Design models are widely spread as core artifacts in software engineering. Yet, a key problem is how to fulfill correctly these blueprint specifications when code components are developed. The best possible scenario occurs when a source modeling language can be perfectly linked to a target language of election. Namely, a well defined mapping bridges the gap between the source and the target language. Otherwise, manual encoding of the system design is cumbersome and error prone. In this setting, we introduce a SQL-PL1 code generator for OCL expressions that, in contrast to other proposals, is able to map OCL iterate and iterator expressions thanks to our use of stored procedures. More in detail, our source language is the Object Constraint Language (OCL), which nowadays is an ISO standard used to express constraints and queries in a textual notation on UML models. Our target language is the procedural language (PL) extension to the Structured Query Language (SQL). SQL is a special-purpose programming language designed for managing data in relational database management systems (RDBMS). The purpose of PL for SQL is to combine database language and procedural programming language. Although SQL is also an ISO standard, different RDBMS implement certain syntactic variations to the standard SQL notation. Thus, we had to adapt the implementation of our mapping to each of them. As implementation targets we selected MariaDB, PostgreSQL, and MS SQL Server. MariaDB and PostgreSQL were selected because they are open source and widely used by developers. MS SQL server was selected to be able to compare evaluation time from open source to commercial RDBMS. A variety of applications arises for a mapping from OCL to SQL expressions. Among others, there are three prominent types. These are i) evaluation of OCL expressions (analysis queries and metrics) on large model's instances, ii) identification of constraints during data modeling that have to be checked as integrity constraints on actual data; iii) automatic code generation from models. Indeed, our implementation was used as a key component of a toolkit that automatically generated ready-to-deploy web applications for secure data management from design models. Our component mapped and evaluated OCL constraints specified within authorization policies. Our code generator is defined recursively over the structure of OCL expressions and it is implemented in the SQL-PL4OCL tool that is publicly available at [1]. The seminal work of the mapping presented here can be found in [2], [3]. The key idea that enables the mapping from OCL iterator expressions to iterative stored procedures remains the same, but the work detailed in [4] introduces a novel mapping from OCL expressions to SQL-PL stored procedures. In the novel mapping we have taken design decisions which have facilitated the recursive definition of the code generator and simplified its definition. These decisions have also helped to significantly decrease the time required for the evaluation of the code generated. Regarding semantics, the new mapping is able to deal properly with the three-valued evaluation semantics of OCL. In addition, our original work and implementation was intended only for the procedural extension of MySQL, while our new definition eased the implementation of the mapping into other relational database management systems. In turn, we can now evaluate the resulting code using different RDBMS, which permits us to widen our discussion regarding efficiency in terms of evaluation-time of the code produced by SQL-PL4OCL tool.

Speech-based inputs have become popular in many applications on constrained device environments such as smartphones and tablets, and even personal conversational assistants such as Siri, Alexa, and Cortana. Inspired by this recent success of speech-driven interfaces, in this work, we consider an important fundamental question: How should one design a speech-driven system to query structured data? Recent works have studied new querying modalities like visual [4, 8], touch-based [3, 7], and natural language interfaces (NLIs) [5, 6], especially for constrained querying environments such as tablets, smartphones, and conversational assistants. The commands given by the user are then translated to the Structured Query Language (SQL). But conspicuous by its absence is a speech-driven interface for regular SQL or other structured querying. One might wonder: Why dictate structured queries and not just use NLIs or visual interfaces? From a practical standpoint, many users, including in the C-suite, enterprise, Web, and other domains are already familiar with SQL (even if only a subset of it) and use it routinely. A spoken SQL interface could help them speed up query specification, especially in constrained settings such as smartphones and tablets, where typing SQL would be painful. More fundamentally, there is a trade-off inherent in any query interface, as illustrated in Figure 1(A).

Every organisation needs information flows to operate. Information modelling enables the analysis, communication and optimisation of information flows. LINQ is a technique for information modelling. This paper discusses the LINQ technique for modelling organisational information flows. We illustrate the technique with a case study. Then we analyse the similarities and differences between LINQ and other well-known information modelling techniques. The paper concludes by discussing the potential applications, future directions, and limitation of the LINQ technique.

Fault localization techniques have been applied to database and data-centric applications that use SQL or SQL-based languages. However, existing techniques can only identify the SQL statements that have faults, but not determine the precise location of the faults within SQL statements. Since SQL statements can be rather complex, programmers are still left with a difficult repair chore. We propose a novel fault localization method to localize multiple types of faults in SQL predicates, that is based on row-based dynamic slicing and delta debugging. Our method was implemented in a tool called ALTAR, and experiments were performed on two publicly available databases. Our method can be compared with existing fault localization techniques when these are applied to "drill-down" in SQL statements. The results showed that ALTAR can discover more types of faults. Moreover, for the type of faults discovered by current methods, ALTAR is more precise.

Regardless of the type of application you are creating using the .NET platform, your program will certainly need to access some form of data as it executes. To be sure, data can be found in numerous locations, including XML files, relational databases, in-memory collections, and primitive arrays. Historically speaking, based on the location of said data, programmers needed to make use of different and unrelated APIs. The Language Integrated Query (LINQ) technology set, introduced initially in .NET 3.5, provides a concise, symmetrical, and strongly typed manner to access a wide variety of data stores. In this chapter, you will begin your investigation of LINQ by focusing on LINQ to Objects.

Traditionally, software systems used different methods of accessing data from different datastores. For example, in order to access data from relational databases, flat files, and XML files, developers needed to learn and master different object models. These object models were often totally different from each other in terms of usage syntax and underlying philosophy. Beginning with the .NET Framework 3.5, Microsoft introduced Language Integrated Query (LINQ) technology that simplifies the hassle involved while consuming different sources of data. LINQ comes in three main flavors: LINQ to Objects, LINQ to ADO.NET, and LINQ to XML.

Reusing the data from healthcare information systems can effectively facilitate clinical trials (CTs). How to select candidate patients eligible for CT recruitment criteria is a central task. Related work either depends on DBA (database administrator) to convert the recruitment criteria to native SQL queries or involves the data mapping between a standard ontology/information model and individual data source schema. This paper proposes an alternative computer-aided CT recruitment paradigm, based on syntax translation between different DSLs (domain-specific languages). In this paradigm, the CT recruitment criteria are first formally represented as production rules. The referenced rule variables are all from the underlying database schema. Then the production rule is translated to an intermediate query-oriented DSL (e.g., LINQ). Finally, the intermediate DSL is directly mapped to native database queries (e.g., SQL) automated by ORM (object-relational mapping).

Most business applications store data in some kind of datastore, which is usually a relational database. To that end, SQL Server is one of Microsoft’s flagship products. Since many applications rely on the XML data, that Microsoft found it necessary to incorporate strong support for XML in their database engine.

The volume of spatial data increased tremendously, and growing attention has been paid to the research of distributed system for spatial data analysis. Spark, an in-memory distributed system which performs much better than Hadoop in speed and many other aspects, lacks spatial SQL query extensions. In this paper, we study the technology framework of Spark SQL, and implement the spatial query extension system tightly combined with the native Spark system. The extensions in the system include spatial types, spatial operators, spatial query optimizations and spatial data source formats. The spatial extension system on Spark retains the scalability and can be further extended with more query optimizations and data source formats. In this paper, the spatial data type system and spatial operator system follow OGC standards. In addition, the extension method is also a general method of query extensions on Spark SQL in other fields.

LINQ is a feature of C# introduced in .NET 3.5. It lets you work with different types of data and provides an easy and powerful way to write and maintain queries.

Databases are a tool to persist data that you intend to refer to regularly and need to keep for a long time.

As SQL moved from the English-like language for ad hoc queries by business users to its present status as the universal relational database access, the lack of abstractions and compositionality in the original design is felt more and more acute. Recently added subqueries and common table expressions compensate, albeit generally inefficiently. The inadequacies of SQL motivated language-integrated query systems such as (T-)LINQ, which offer an applicative, programming-like query language compiled to efficient SQL.

The research presented in this paper supports the identification of common subexpressions as candidates for potential materialized views that form the basis of multiple query optimization in a loosely-coupled distributed system where query expressions access heterogeneous data sources, including relations and data-centric XML. This paper introduces a unifying mixed multigraph formalism to represent SQL, XQuery, and LINQ queries in a common query graph model and a heuristics-based algorithm to detect common subexpressions. The identified common subexpressions represent an opportunity for defining a materialized view to avoid repeating computation. The common subexpressions may access only relations, only XML, or a combination of relations and XML. The mixed multigraph model and the heuristic rules presented in this paper have distinguished advantages over the existing approaches that consider only relational or XML data sources individually. The mixed multigraph model can present SQL, XQuery, and LINQ queries in a single graph model and the heuristic rules are designed to consider the identical and subsumed conditions at the same time. A prototype implementation of the algorithm illustrates the applicability of the approach using various examples from the research literature as well as scenarios over a Criminal Justice enterprise that include common subexpressions across relational and XML data sources.

JOOQ is a query language as derived from SQL or JOOQ is a query language in which SQL uses the objects of OOQ. As there are many issues in integration of java application with various databases via JDBC. This paper proposes a new java library known as JOOQ. The primary aim of JOOQ focuses on integration issues in java programming language. With JOOQ, programmers can write SQL queries in a simpler and faster way. JOOQ thus decreases the programming effort by 10-20% which leads to higher quality of code with low error rate. Thus the application is also significantly light and less prone to errors.

Microsoft China had announced that it would end on a Windows XP system support and maintenance in the April 8, 2014, progress in the new study. NET development environment, further research database development technology is very important. C# in. NET framework most popular development language, compared to other languages, which have simple, convenient, rapid development, etc.[1]. Based on data access technology under. NET Framework, which provides a number of features to improve the operation of data. This article also describes in the latest. NET Framework, a new language inheritance query technology LINQ.

The root cause for confidentiality and integrity attacks against computing systems is insecure information flow . The complexity of modern systems poses a major challenge to secure end-to-end information flow, ensuring that the insecurity of a single component does not render the entire system insecure. While information flow in a variety of languages and settings has been thoroughly studied in isolation, the problem of tracking information across component boundaries has been largely out of reach of the work so far. This is unsatisfactory because tracking information across component boundaries is necessary for end-to-end security. This paper proposes a framework for uniform tracking of information flow through both the application and the underlying database. Key enabler of the uniform treatment is recent work by Cheney et al., which studies database manipulation via an embedded language-integrated query language (with Microsoft's LINQ on the backend). Because both the host language and the embedded query languages are functional F#-like languages, we are able to leverage information-flow enforcement for functional languages to obtain information-flow control for databases "for free", synergize it with information-flow control for applications and thus guarantee security across application-database boundaries. We develop the formal results in the form of a security type system that includes a treatment of algebraic data types and pattern matching, and establish its soundness. On the practical side, we implement the framework and demonstrate its usefulness in a case study with a realistic movie rental database.

Storage is the key link of logistics process, and storage management is the essential for modern enterprises. The system uses Java as development environment, selects SQL server 2005 as database, to realize reasonable stock with high efficiency.

In this paper we examine opportunities arising from the convergence of two trends in data management: in-memory database systems (imdbs), which have received renewed attention following the availability of affordable, very large main memory systems; and language-integrated query, which transparently integrates database queries with programming languages (thus addressing the famous 'impedance mismatch' problem). Language-integrated query not only gives application developers a more convenient way to query external data sources like imdbs, but also to use the same querying language to query an application's in-memory collections. The latter offers further transparency to developers as the query language and all data is represented in the data model of the host programming language. However, compared to imdbs, this additional freedom comes at a higher cost for query evaluation. Our vision is to improve in-memory query processing of application objects by introducing database technologies to managed runtimes. We focus on querying and we leverage query compilation to improve query processing on application objects. We explore different query compilation strategies and study how they improve the performance of query processing over application data. We take C# as the host programming language as it supports language-integrated query through the linq framework. Our techniques deliver significant performance improvements over the default linq implementation. Our work makes important first steps towards a future where data processing applications will commonly run on machines that can store their entire datasets in-memory, and will be written in a single programming language employing language-integrated query and imdb-inspired runtimes to provide transparent and highly efficient querying.

With the rapid development of Internet, more and more organizations connect their databases to the Internet for resource sharing. However, due to developers’ lack of knowledge of all possible attacks, web applications become vulnerable to multiple attacks. Thus the network databases could face multiple threats. Web applications generally consist of a three tier architecture where database is in the third pole, which is the most valuable asset in any organization. SQL injection is an attack technique used to exploit code by altering back-end SQL statements through manipulating input. An attacker can directly compromise the database, that’s why this is a most threatening attack. SQL injection attack occupies first position in top ten vulnerabilities as specified by Open Web Application Security Project [12]. It is probably the most common Website vulnerability today! Current scenarios which provide solutions to SQL injection attack either have limited scope i.e. can’t be implemented in all platforms or do not cover all types of SQL injection attacks. In this work we implement Detection Block model against SQL injection attacks. The model works both on client and server side. Client side implements a filter function and server side is based on information theory. MAC static and dynamic query which is derived from entropy is compared to detect an attack.

We propose a logical framework to analyze complex predicates (those involving a subquery) in SQL. We propose a new operator in the relational algebra for handling such predicates, and study its properties and how it combines with traditional relational operator. We focus on predicates of the form att θ MOD S, where att is an attribute, θ a comparison operator, MOD is one of SOME or ALL, and S is a (correlated or non-correlated) subquery. We provide a formal characterization of these predicate, as well as an implementation and optimization strategies for it. We show that our approach is extendible, so we can support the expression and optimization of other, similar predicates. Finally, we describe experimental evidence that the proposed approach is more efficient than the traditional approach across a variety of conditions.

As an enterprise database product, SQL Server must track space usage status of all data pages in data files. SQL Server uses PFS page to track information of space usage in data pages. This paper discusses the principles of data page space tracking. Performed add, delete, modify, etc. series of experiments, results shown that the PFS data changes corresponding to different data operations.

Considering the feature of high-security and complex business logic of radio program production, combining with the theory of Computer Supported Cooperative Work (CSCW), we designed and implemented a radio program production system with high-security and strong practicability. Based on the C/S structure and .Net platform, the system implements the unity query management of different data sources by applying and extending the Language Integrated Query (LINQ) technology. This paper focuses on the overall architecture, service module and key technologies of the production system.

In this paper, we research the stored procedure combined with the bank CRM system. Mainly introduces how to improve the efficiency of the stored procedure in ORACLEs PL/SQL in the banks CRM system.

In a microservice architecture aggregation of data collected from different sources is a common task. Today’s technology trends require us to exchange data that is no longer tabular. JSON data format has gained popularity among web developers, and has become the main format for exchanging information over the web. When we need to aggregate data collected from the web, storing it into relational database just to perform this task and pass it to the next unit to process or display it often is an exaggerated action. In this paper, we discuss a scenario and an implementation of in-memory preprocessing and aggregating data using lazy evaluation, value tuples and LINQ.

The HEP community is preparing for the LHC’s Run 3 and 4. One of the big challenges for physics analysis will be developing tools to efficiently express an analysis and able to efficiently process the x10 more data expected. Recently, interest has focused on declarative analysis languages: a way of specifying a physicists’ intent and leaving everything else to the underlying system. The underlying system takes care of finding the data - powering the event processing loop – and even exactly how to most efficiently apply a desired jet selection. If this works, this would allow an analyser to test their algorithm on a small amount of data on their GPU-less laptop and then run it on a large amount of data on a server with multiple large GPU’s without having to alter their code. The LINQToROOT project, started almost seven years ago, fits this model. It has been used and tested in three ATLAS published analyses. LINQToROOT is based on the Language Integrated Query system built into the cross-platform C# language. It enables writing strongly-typed queries on a ROOT’s TTree’s data and transcribes the data to a C++ algorithm that can run in ROOT. Recent work on this system has had two goals: improving analysis efficiency and better understanding the requirements of a declarative analysis language. For example, a good analysis language should be able to abstract away the backend – recent work has increased the possible back ends from formerly the single Windows ROOT backend to one that runs on Linux, the Windows Linux-subsystem, and an experimental one that allows for PROOF like parallel processing – all done with almost no change to the analysis code itself. Any analysis language must also be rich enough to support an experiment’s data model. To test this, some experiments with the full ATLAS xAOD data model have been performed. All of this has been done while attempting to keep the project close to its original goals: quick turnaround for real ATLAS physics analysis. This work will be discussed in some detail along with thoughts and lessons that have helped shape our thinking about an Analysis Language and perhaps our approach to future physics analysis employing declarative analysis.

Language-integrated query is receiving renewed attention, in part because of its support through Microsoft's LINQ framework. We present a practical theory of language-integrated query based on quotation and normalisation of quoted terms. Our technique supports join queries, abstraction over values and predicates, composition of queries, dynamic generation of queries, and queries with nested intermediate data. Higher-order features prove useful even for constructing first-order queries. We prove a theorem characterising when a host query is guaranteed to generate a single SQL query. We present experimental results confirming our technique works, even in situations where Microsoft's LINQ framework either fails to produce an SQL query or, in one case, produces an avalanche of SQL queries.

Using the internet to provide learning exchange services for students has become a need now. This paper introduces the frame structure and the main functions of each module of the campus online learning system with a three-layered B/S structure based on .NET and Microsoft SQL Server 2000, and lays stress on implementing the course management and courseware management of the system.

This article describes the information extraction in information system from SQL Server. Through the system tables all information of SQL Server's internal objects in information system can be obtained, including databases, data tables, stored procedures and triggers and so on. Analysis the method of the object’s information extraction in information system. Describe the relationships of system tables in SQL Server through the graph and table in detail. A visual basic procedure using system table method is designed to achieve the table’s structure information for all user tables in specified SQL Server database. This method is more complete than the method based on ADOX.

SQL Server skill assessment is deficient. This article describes the SQL Server object model SQL-DMO. Propose the process of SQL Server skill assessment: test paper generation, Skill assessment and Automatic marking. Analysis two key techniques of skill assessment based on SQL-DMO. One is getting operating information based on SQL-DMO. The other is automatic marking for Skill and Operation. Besides, automatic marking rules for database object creating, modifying and deleting are focused on. The author uses SQL-DMO method to realize database object information acquisition, and implement automatic marking for SQL Server database skill assessment.

Use of the term ‘Cloud-Computing’ has become increasingly widespread in recent years. Many companies such as Yahoo and Google have been marketing cloud-based services to the business community and to individual users. In addition to the Structure Query Language (SQL) technique, Map–Reduce has recently emerged as a key programming model that realizes large-scale data processing for cloud computing. Many real-world tasks, such as data processing for search engines, can be implemented in parallel through a simple interface with two basic functions – Map and Reduce. This article conducts practical evaluations to compare the performance of the Hadoop implementation of Map–Reduce with that of an SQL Server, with results indicating that Hadoop can complete the same query faster than an SQL Server. Some significant factors were also tested to determine their effect on Hadoop performance, which was determined to be superior, especially for large-scale datasets with the use of additional machines for data processing.

Data aggregation is a process of expressing information in a summary form for further interpretation using statistical analysis, machine learning or data mining. Almost every data manipulation language has built-in, standard or common capabilities to aggregate data. This paper overviews different concepts of expressing data aggregation in programming languages. Specifically, we overview data aggregation in declarative SQL-based languages like T-SQL, PL/SQL, PostgreSQL, MDX, Neo4j, imperative C# LINQ and R. These concepts affected our domain-specific query language which we designed and implemented for metallurgical production.

To extend the capability of big data analytics in SQL databases, we propose an interaction protocol and communication framework called Dex. Using Dex protocol, we can integrate a SQL database system and a big data system into a unified data analysis platform. The integrated system allows users to call complex analytics functions available in the big data analytics system with a simple SQL statement. We prototype our idea on Postgresql and Spark and demostrate promising performance gains against pure SQL UDF solutions.

SQL (structure query language) injection is one of threat to the application connected to the database. By implementing SQL injection attacker can gain full access to the application or database so that it can remove even significant data irresponsibly. Applications that do not validate the user's input appropriately make them vulnerable against SQL injection. Various methods have been developed to prevent SQL injection each with advantages and disadvantages. Implementation of PDO (php data object) Parameterized Query properly can prevent SQL injection. PDO not only provides a method to facilitate the implementation of parameterized queries but also makes the code is portable because the PDO can be used on multiple databases. This paper describes the results of research on the use of PDO Parameterized Query to minimize SQL Injection on the new scheduling application. The uniqueness of this study lies in the incorporation of PDO Parameterized Query with client-side web technology (indexedDB). By using PDO Parameterized Query on this application, making it is not vulnerable to attack that caused by SQL injection.

For the issues that the Web service is easy to suffer from SQL injection attacks in cloud computing environment. This paper proposes a kind of SQL detection method which combined with dynamic taint analysis and input filtering. And it is embedded in the cloud environment to achieve the protection of the Web applications in cloud deployment. First, the method obtains the SQL keywords through the analysis of lexical regulation for SQL statement. Then, it analyses the syntax regulation of SQL statement to create the rule tree. Finally, it traverses ternary tree on the basis of the model which established by SQL syntax regulation to detect the attacks. Experimental results show that the method is effective and feasible. Also, the accuracy is improved by adding the detection module.

This paper presents a technique for weaving temporal semantics into an SQL query. We assume that a query writer programs a query and then annotates the query with some temporal semantics, such as sequenced or nonsequenced semantics. The annotation is a lightweight temporal statement modifier, it changes the semantics by which the query is evaluated. Annotations can be specified for a wide variety of semantics including sequenced and nonsequenced semantics. We give a denotational semantics for translating SQL queries with temporal annotations into Nested SQL. Nested SQL is SQL with some additional operations. We also describe how the translation is implemented using an ANTLR grammar for SQLite.

Existing vulnerabilities of Web system threaten the regular work of information systems. The most common Web system vulnerability is SQL injection. There is known approaches to protect Web applications against SQL injection attacks in the article. To improve the Web software security it is developed defense mechanism that protects Web resources from SQL injection performing. To implement this software it is used PHP, JavaScript and formal language theory known as regular expression. As a result it is received a software tool which allows to protect Web software from SQL injection vulnerability. Developed software tool allows user to protect his own Web application from an attack with using SQL.

We have proposed a framework named Rough Non-deterministic Information Analysis (RNIA), and developed two software tools, RNIA in Prolog and getRNIA in Python. In order to handle big data sets, we newly employ SQL and PHP. This paper reports the current state of the software tool in SQL, which is the preliminary version for NIS-Apriori in SQL.

We describe the design and implementation of the new Live Query Statistics (LQS) feature in Microsoft SQL Server 2016. The functionality includes the display of overall query progress as well as progress of individual operators in the query execution plan. We describe the overall functionality of LQS, give usage examples and detail all areas where we had to extend the current state-of-the-art to build the complete LQS feature. Finally, we evaluate the effect these extensions have on progress estimation accuracy with a series of experiments using a large set of synthetic and real workloads.

Numerous databases promoted as SQL-on-Hadoop, NewSQL and NoSQL support semi-structured, schemaless and heterogeneous data, typically in the form of enriched JSON. They also provide corresponding query languages. In addition to these genuine JSON databases, relational databases also provide special functions and language features for the support of JSON columns, typically piggybacking on non-1NF (non first normal form) features that SQL acquired over the years. We refer to SQL databases with JSON support as SQL/JSON databases. The evolving query languages present multiple variations: Some are superficial syntactic ones, while other ones are genuine differences in modeling, language capabilities and semantics. Incompatibility with SQL presents a learning challenge for genuine JSON databases, while the table orientation of SQL/JSON databases often leads to cumbersome syntactic/semantic structures that are contrary to the semistructured nature of JSON. Furthermore, the query languages often fall short of full-fledged semistructured query language capabilities, when compared to the yardstick set by XQuery and prior works on semistructured data (even after superficial model differences are abstracted out). We survey features, the designers' options and differences in the approaches taken by actual systems. In particular, we first present a SQL backwards-compatible language, named SQL++, which can access both SQL and JSON data. SQL++ is expected to be supported by Couchbase's CouchDB and UCI's AsterixDB semistructured databases. Then we expand SQL++ into the Configurable SQL++, whereas multiple possible (and different) semantics are formally captured by the multiple options that the language's semantic configuration options can take. We show how appropriate setting of the configuration options morphs the Configurable SQL++ semantics into the semantics of 10 surveyed languages, hence providing a compact and formal tool to understand the essential semantic differences between different systems. We briefly comment on the utility of formally capturing semantic variations in polystore systems. Finally we discuss the comparison with prior nested and semistructured query languages (notably OQL and XQuery) and describe a key aspect of query processor implementation: set-oriented semistructured query algebras. In particular, we transfer into the JSON era lessons from the semistructured query processing research of the 90s and 00s and combine them with insights on current JSON databases. Again, the tutorial presents the algebras' fundamentals while it abstracts away modeling differences that are not applicable.

As the data-driven economy evolves, enterprises have come to realize a competitive advantage in being able to act on high volume, high velocity streams of data. Technologies such as distributed message queues and streaming processing platforms that can scale to thousands of data stream partitions on commodity hardware are a response. However, the programming API provided by these systems is often low-level, requiring substantial custom code that adds to the programmer learning curve and maintenance overhead. Additionally, these systems often lack SQL querying capabilities that have proven popular on Big Data systems like Hive, Impala or Presto. We define a minimal set of extensions to standard SQL for data stream querying and manipulation. These extensions are prototyped in SamzaSQL, a new tool for streaming SQL that compiles streaming SQL into physical plans that are executed on Samza, an open-source distributed stream processing framework. We compare the performance of streaming SQL queries against native Samza applications and discuss usability improvements. SamzaSQL is a part of the open source Apache Samza project and will be available for general use.