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Today's database management systems implement sophisticated access control mechanisms to prevent unauthorized access and modifications. For instance, this is an important basic requirement for SOX (Sarbanes‐Oxley Act) compliance, whereby every past transaction has to be traceable at any time. However, malicious database administrators may still be able to bypass the security mechanisms in order to make hidden modifications to the database. This paper aims to address these issues.

In this paper the authors define a novel signature of a B⁺‐tree, a widely‐used storage structure in database management systems, and propose its utilization for supporting the logging in databases. This additional logging mechanism is especially useful in conjunction with forensic techniques that directly target the underlying tree‐structure of an index. Several techniques for applying this signature in the context of digital forensics on B⁺‐trees are proposed in the course of this paper. Furthermore, the authors' signature can be used to generate exact copies of an index for backup purposes, thereby enabling the owner to completely restore data, even on the structural level.

For database systems in enterprise environments, compliance to regulatory standards such as SOX (Sarbanes‐Oxley Act), whereby every past transaction has to be traceable at any time, is a fundamental requirement. Today's database management systems usually implement sophisticated access control mechanisms to prevent unauthorized access and modifications. Nonetheless malicious database administrators would be able to bypass the security mechanisms in order to make modifications to the database, while covering their tracks.

In this paper, the authors demonstrate how the tree structure of the underlying store engine can be used to enhance forensic logging mechanisms of the database. They define a novel signature for B⁺‐trees, which are used by the InnoDB storage engine. This signature stores the structure of database storage files and can help in reconstructing previous versions of the file for forensic purposes. Furthermore, the authors' signature can be used to generate exact copies of an index for backup purposes, thus enabling the owner to completely restore data, even on the structural level. The authors applied their concept to four real‐life scenarios in order to evaluate its effectiveness.

Nature-based solutions (NBS) generate different impacts at the urban scale, such as the ability to regulate water or store carbon, comparable to traditional, gray infrastructures in a more cost-efficient way. On the other hand, by their intrinsic nature, NBS do deliver a series of other services that are commonly defined as social, economic, and environmental cobenefits. These benefits are not always valued in a consistent and complete way, so there is the need to compile a more comprehensive evidence base on the social, economic, and environmental effectiveness of NBS. The chapter attempts to identify a categorization of the existing NBS and define the ecosystem services (ES) provided by them. Furthermore, starting from the results achieved through the definition of the existing NBS frameworks assessment, the chapter will identify a set of key performance indicators KPIs, based on the ES produced by NBS, to measure the economic, social, and environmental benefits generated in by NBS at the urban level taking into account their multifunctional character. In total, 66 key performance indicators have been individuated: 3 for provisioning services, 38 for regulating services, 17 for cultural services, and 8 for supporting services. Each indicator has been associated to a category of ES in order to measure and evaluate the performances of NBS implemented in cities.

This paper revolves around the usage of data analytics in the Qur’an and Hadith through a new text mining technique to answer the main research question of whether the activities and the data flows of the Murabaha financing contract is compatible with Sharia law. The purpose of this paper is to provide a thorough and comprehensive database that will be used to examine existing practices in Islamic banks’ and improve compliancy with Islamic financial law (Sharia).

To design a Sharia-compliant Murabaha business process originated on text mining, the authors start by identifying the factors deemed necessary in their text mining techniques of both texts; using a four-step strategy to analyze those text mining analytics; then, they list the three basic approaches in text mining used for new knowledge discovery in databases: the co-occurrence approach based on the recursive co-occurrence algorithm; the machine learning or statistical-based; and the knowledge-based. They identify any variation and association between the Murabaha business processes produced using text mining against the one developed through data collection.

The main finding attained in this paper is to confirm the compatibility of all activities and the data flows in the Murabaha financing contract produced using data analytics of the Quran and Hadith texts against the Murabaha business process that was developed based on data collection. Another key finding is revealing some shortcomings regarding Islamic banks business process compliance with Sharia law.

Given Murabaha as the most popular mode of Islamic financing with more than 75% in total transactions, this research has managed to touch-base on an area that is interesting to the vast majority of those dealing with Islamic finance instruments. By reaching findings that could improve the existing Islamic Murabaha business process and concluding on Sharia compliance of the existing Murabaha business process, this research is quite relevant and could be used in practice as well as in influencing public policy. In fact, Islamic Sharia law experts, Islamic finance professionals and Islamic banks may find the results of this study very useful in improving at least one aspect of the Islamic finance transactions.

By using a novel, fresh text mining methods built on recursive occurrence of synonym words from the Qur’an and Hadith to enrich Islamic finance, this research study can claim to have been the first of its kind in using machine learning to mine the Quran, Hadith and in extracting valuable knowledge to support and consolidate the Islamic financial business processes and make them more compliant with the i.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

The library CD‐ROM database with its enormous storage and retrieving from network capabilities, has been gradually replacing some of its printed counterparts. But one of the disadvantages is that it can only access the full‐text image CD‐ROM database via local area network environment. This paper proposes a new method to solve this problem. The method uses hash function as data structure and image document process technique to perform a practical library full‐text image CD‐ROM database retrieving system. The results of its function can reduce users storage space, allow multi‐users to retrieve the same CD‐ROM database, and allow users to retrieve and print out full text image CD‐ROM database via campus network.

The literature on the facilities location problem is quite extensive with a wide variety of solution methods for addressing these problems where the objective is cost minimization. Develops a branch and bound algorithm for solving the uncapacitated, multi‐period facility location problem where the objective is to maximize profits. The solution method uses a number of simplification and branching decision rules to solve the problem efficiently. Extensive computational results on the algorithm’s performance are provided. The results indicate that the algorithm provides optimal solutions in substantially less time than LINDO.

The paper aims to resolve three major issues in location-based applications (LBA) known as heterogeneity, user privacy, and context-awareness by proposing an elastic and open design platform named OpenLS privacy-aware middleware (OPM) for LBA.

The paper analyzes relevant approaches ranging from both academia and mobile industry community and insists the importance of heterogeneity, user privacy, and context-awareness towards the development of LBA.

The paper proposes the OPM by design. As a result, the OPM consists of two main component named application middleware and location middleware, which are cooperatively functioned to achieve the above goals. In addition, the paper has given the implementation of the OPM as well as its experiments. It is noted that two privacy-preserving techniques at two different levels are integrated into the OPM, including Memorizing algorithm at the application level and Bob-tree at the database level. Last but not least, the paper shows further discussion about other problems and improvements that might be needed for the OPM.

Each issue has its sub problems that cause more influences to the OPM. Besides, each of the issues requires more investigations in depth in order to have better solutions in detail. Therefore, more overall experiments should be conducted to assure the OPM's scalability and effectiveness.

The paper hopefully promotes and speeds up the development of LBA when providing the OPM with suitable application programming interfaces and conforming the OpenLS standard.

This paper shows its originality towards location-based service (LBS) providers to develop their applications and proposes the OPM as a unified solution dealing with heterogeneity, user privacy, and context-awareness in the world of LBS.

The purpose of this paper is to present a two‐phase approach for designing an efficient tailored but flexible storage solution for resource description framework (RDF) data based on its query workload characteristics.

The approach consists of two phases. The vertical partitioning phase which aims of reducing the number of join operations in the query evaluation process, while the adjustment phase aims to maintain the efficiency of the performance of the query processing by adapting the underlying schema to cope with the dynamic nature of the query workloads.

The authors perform comprehensive experiments on two real‐world RDF datasets to demonstrate that the approach is superior to the state‐of‐the‐art techniques in this domain.

The main motivation behind the authors' approach is that several benchmarking studies have recently shown that each RDF dataset requires a tailored table schema in order to achieve efficient performance during query processing. None of the previous approaches have considered this limitation.

A comparison was made between CDS/ISIS, its Windows version WINISIS, and InMagic’s INMAGIC and DB/TextWorks software. Packages were evaluated for their database creation, information retrieval and report production capabilities. Windows versions were found to provide significant enhancements over DOS versions of software. The evaluation aimed to determine the advantages to a developing country of creating bibliographic databases using commercial software.