Database-driven Web Sites Research Articles

Performance Analysis in Memory Distribution Key Value Store and Design of Storage Driver for SBK Framework

This paper aims to write the Memcached storage driver and performance analysing the Memcached. Memcached is a free and open source software, high performance, distributed memory object caching system. It is often used to speed up dynamic database-driven websites by caching data and objects in RAM to reduce the number of times, an external data source must be read. The SBK itself is a high- performance benchmark tool/framework. It massively writes the data to the storage system and reads the data from the storage system. The SBK supports multi writers and readers and also the End to End latency benchmarking. The main objective is to performance benchmark the Memcached storage driver using Storage Benchmark Kit(SBK), to speed up the performance of websites and web applications using SBK software, to extract the results of the operations which were performed on the data. The performance benchmarking is done by adding SBK driver which specifies the input/output operations of storage systems. SBK delivers the throughput and latency values for a every specific interval for live performance analytics. Memcached is a free and open-source software, high performance, distributed memory object caching system. The SBK itself is a high-performance benchmark tool/framework. The SBK supports multi writers and readers and also the End-to-End latency benchmarking. Memcached is a key-value store that keeps data in the DRAM i.e., data is not persistent. Clients communicate with Memcached through a simple set of APIs: Set, Add, replace are used to store data, Get or Remove are used to retrieve or remove data.

Detecting The Normal And Injected Sql Query Using Random Forest Classification With Rabin Carp Pattern Matching Algorithm In Web Database

Nowadays detection of SQL injection attack is becoming very important in database-driven websites. SQL-Injection attack is a class of attacks that many of these systems are highly vulnerable to, and there is no known fool-proof defense against various attacks. In order to overcome this problem, the assurance for security to all the web Sites is essential for the protections for their database. Various Companies facing challenges in intrusion and Detection while installation and deploying their sites. And most common attack in the way if requesting the input as malicious logic in sql query of the authentication page. So that query returns success response in the condition of functionality. In this paper, we proposed the two algorithms such as Random forest and Rabin carp pattern matching to detect the injected query attack. Random forest is the technique used to detect the anomaly queries from the dataset. And Rabin carp pattern matching is used to prevent SQL injection in authentication page.

A Pure Visual Approach for Automatically Extracting and Aligning Structured Web Data

Database-driven websites and the amount of data stored in their databases are growing enormously. Web databases retrieve relevant information in response to users’ queries; the retrieved information is encoded in dynamically generated web pages as structured data records. Identifying and extracting retrieved data records is a fundamental task for many applications, such as competitive intelligence and comparison shopping. This task is challenging due to the complex underlying structure of such web pages and the existence of irrelevant information. Numerous approaches have been introduced to address this problem, but most of them are HTML-dependent solutions that may no longer be functional with the continuous development of HTML. Although a few vision-based techniques have been introduced, various issues exist that inhibit their performance. To overcome this, we propose a novel visual approach, i.e., programming-language-independent, for automatically extracting structured web data. The proposed approach makes full use of the natural human tendency of visual object perception and the Gestalt laws of grouping. The extraction system consists of two tasks: (1) data record extraction, where we apply three of the Gestalt laws (i.e., laws of continuity, proximity, and similarity), which are used to group the adjacently aligned visually similar data records on a web page; and (2) data item extraction and alignment, where we employ the Gestalt law of similarity, which is utilized to group the visually identical data items. Our experiments upon large-scale test sets show that the proposed system is highly effective and outperforms the two state-of-art vision-based approaches, ViDE and rExtractor. The experiments produce an average F1 score of 86.02%, which is approximately 55% and 36% better than that of ViDE and rExtractor for data record extraction, respectively; and an average F1 score of 86.19%, which is approximately 39% better than that of ViDE for data item extraction.

Using web2py Python framework for creating data-driven web applications in the academic library

Purpose – Many libraries have a need to develop their own data-driven web applications, but their technical staff often lacks the required specialized training – which includes knowledge of SQL, a web application language like PHP, JavaScript, CSS, and jQuery. The web2py framework greatly reduces the learning curve for creating data-driven websites by focussing on three main goals: ease of use; rapid development; and security. web2py follows a strict MVC framework where the controls and web templates are all written in pure Python. No additional templating language is required. The paper aims to discuss these issues. Design/methodology/approach – There are many frameworks available for creating database-driven web applications. The author had used ColdFusion for many years but wanted to move to a more complete web framework which was also open source. Findings – After evaluating a number of Python frameworks, web2py was found to provide the best combination of functionality and ease of use. This paper focusses on the strengths of web2py and not the specifics of evaluating the different frameworks. Practical implications – Librarians who feel that they do not have the skills to create data-driven websites in other frameworks might find that they can develop them in web2py. It is a good web application framework to start with, which might also provide a gateway to other frameworks. Originality/value – web2py is an open source framework that could have great benefit for those who may have struggled to create database-driven websites in other frameworks or languages.

Data-mining based SQL injection attack detection using internal query trees

Detecting SQL injection attacks (SQLIAs) is becoming increasingly important in database-driven web sites. Until now, most of the studies on SQLIA detection have focused on the structured query language (SQL) structure at the application level. Unfortunately, this approach inevitably fails to detect those attacks that use already stored procedure and data within the database system. In this paper, we propose a framework to detect SQLIAs at database level by using SVM classification and various kernel functions. The key issue of SQLIA detection framework is how to represent the internal query tree collected from database log suitable for SVM classification algorithm in order to acquire good performance in detecting SQLIAs. To solve the issue, we first propose a novel method to convert the query tree into an n-dimensional feature vector by using a multi-dimensional sequence as an intermediate representation. The reason that it is difficult to directly convert the query tree into an n-dimensional feature vector is the complexity and variability of the query tree structure. Second, we propose a method to extract the syntactic features, as well as the semantic features when generating feature vector. Third, we propose a method to transform string feature values into numeric feature values, combining multiple statistical models. The combined model maps one string value to one numeric value by containing the multiple characteristic of each string value. In order to demonstrate the feasibility of our proposals in practical environments, we implement the SQLIA detection system based on PostgreSQL, a popular open source database system, and we perform experiments. The experimental results using the internal query trees of PostgreSQL validate that our proposal is effective in detecting SQLIAs, with at least 99.6% of the probability that the probability for malicious queries to be correctly predicted as SQLIA is greater than the probability for normal queries to be incorrectly predicted as SQLIA. Finally, we perform additional experiments to compare our proposal with syntax-focused feature extraction and single statistical model based on feature transformation. The experimental results show that our proposal significantly increases the probability of correctly detecting SQLIAs for various SQL statements, when compared to the previous methods.

Database-driven web sites, ed. by Kristin Antelman

The content of this book covers mainly organising the information in your library to make it available to Internet users. It includes the range of software solutions that has emerged, and examines the challenges of using this software to develop web sites that make information access user-friendly. These web sites are all database-driven rather than HTMLdependent.

Third int'l workshop on "personalized access, profile management, and context awareness in databases" (PersDB 2009)

Proliferation of database-driven web sites has brought upon aplethora of information access and dissemination applications. Mon-itoring and trading stock portfolios, news notification, weather track-ing, and even simple search are just a few examples. In addition,emerging applications such as Web-based communities, wikis, so-cial networks, mashups and folksonomies enhance creativity, in-formation sharing, and collaboration among users providing richerinteraction possibilities. Now, users can not only access content butthey can also generate, share and modify content (both theirs andothers’), compose their applications, enhance their interface, etc.In all these applications, different notions of user information,such as preferences, community memberships and social interac-tions, and context information, such as a user’s social network,location, time, and other features of a user’s environment, are ofparamount importance in order to improve and personalize user ex-perience. In this context, new challenges emerge for user-centric,context-aware database systems for storing and managing differ-ent aspects of user and context information, for data managementand computing taking into account personal, social and contextualinformation about users and for customizability of their behavior.There are several research efforts that are trying to address re-search problems that arise when developing such systems. Forexample, user preferences may be associated with the search pro-cess and interpreted as preferences over the desired system answers(e.g., [3, 4]) or they may be associated with aspects of the data man-agement process specifying user requirements for response times,data freshness, quality of service etc, and used in mechanisms,such as caching, query admission control, resource allocation, andscheduling (e.g., [2, 5]). Likewise, a user’s context can be the socialnetwork or community where a user belongs. A user may belongto more than one network and community. Identification of a user’community is important as well as building personalized recom-mendations, user collaboration and sharing schemes that addresscommunities [1, 6].The purpose of the PersDB Workshop (“Personalized Access,Profile Management, and Context Awareness in Databases”) is tobring together researchers and practitioners both from the academiaand the industry and provide a forum for presentation of the latestresearch results, new technology developments, and new applica-tions in the areas of personalized access, profile management, andcontext awareness in database systems.The 3

How best to build web-scale data managers?

Many of the largest database-driven web sites use custom web-scale data managers (WDMs). On the surface, these WDMs are being applied to problems that are well-suited for relational database systems. Some examples are the following: • Map-Reduce [5], Hadoop [7], and Dryad [9] are used to process queries on large data sets using sequential scan and aggregation. Hive [8] is a data warehouse built on Hadoop. • Google's Bigtable [3] is used to store a replicated table of rows of semi-structured data. • Amazon's Dynamo [6] is used to store partitioned, replicated databases of key-value pairs. Cassandra [2] is similar. • Object caching systems are used instead of a persistent store, such as memcached [10], Oracle's Coherence, and Microsoft's Velocity project.

Multitiered Cache Management and Acceleration for Database-Driven Websites

Response time is a key differentiation point among electronic commerce (e-commerce) applications. For many e-commerce applications, web pages are created dynamically based on the current state of a business stored in database systems. To improve the response time, many e-commerce websites deploy caching solutions for acceleration of content delivery. There are multiple tiers in the content delivery infrastructure where cache servers can be deployed, including (1) data caching (in data centers), (2) content page caching (in edge or front end caches), and (3) database query result set caching (between application servers and DBMS). The architecture of database-driven e-commerce websites is more complex than that of typical websites. It requires the integration of web servers, application servers, and back end database systems as well as dynamic content caching solutions. In this paper, the issues associated with the management of content page caching and database query result set caching tiers are studied. It is observed that caching management for these two tiers have their unique characteristics. It is because cached object types and information available for the caching management in the two tiers are different. Solutions are proposed for effective caching management for each tier and extensive evaluations are conducted. The experimental results show the usefulness of the technology in improving overall system performance.

Evaluations of architectural designs and implementation for database-driven web sites

Response time is a key differentiation point among electronic commerce (e-commerce) applications. For many e-commerce applications, Web pages are created dynamically based on the current business state, stored in database systems. The architecture of database-driven e-commerce Web sites are more complex than that of typical Web sites. It requires integration of Web servers, application servers, and back-end database systems as well as synchronization of multiple databases if caches are used for acceleration of content delivery. In this paper, we analyze the factors that impact the performance and scalability of a database-driven Web site. We experimentally test (1) the performance metrics of database update, query, and synchronization; (2) trigger overhead; and (3) application server (AS) and database connection overhead and constraints. We describe several architectural design approaches for database-driven Web sites and present experimental results on their performance under various conditions, including varying request rates, update-to-request ratio, cache hit ratio, number of regional AS/data cache suites deployed, and database size. We also discuss how to handle Web page requests that involve SSL, cookies, and fragment pages (i.e. by frame or JSP).