Flexibility in Data Management

With the ongoing expansion of information technology, new fields of application requiring data management emerge virtually every day. In our knowledge culture increasing amounts of data and work force organized in more creativity-oriented ways also radically change traditional fields of application and question established assumptions about data management. For instance, investigative analytics and agile software development move towards a very agile and flexible handling of data. As the primary facilitators of data management, database systems have to reflect and support these developments. However, traditional database management technology, in particular relational database systems, is built on assumptions of relatively stable application domains. The need to model all data up front in a prescriptive database schema earned relational database management systems the reputation among developers of being inflexible, dated, and cumbersome to work with. Nevertheless, relational systems still dominate the database market. They are a proven, standardized, and interoperable technology, well-known in IT departments with a work force of experienced and trained developers and administrators. This thesis aims at resolving the growing contradiction between the popularity and omnipresence of relational systems in companies and their increasingly bad reputation among developers. It adapts relational database technology towards more agility and flexibility. We envision a descriptive schema-comes-second relational database system, which is entity-oriented instead of schema-oriented; descriptive rather than prescriptive. The thesis provides four main contributions: (1)~a flexible relational data model, which frees relational data management from having a prescriptive schema; (2)~autonomous physical entity domains, which partition self-descriptive data according to their schema properties for better query performance; (3)~a freely adjustable storage engine, which allows adapting the physical data layout used to properties of the data and of the workload; and (4)~a self-managed indexing infrastructure, which autonomously collects and adapts index information under the presence of dynamic workloads and evolving schemas. The flexible relational data model is the thesis\' central contribution. It describes the functional appearance of the descriptive schema-comes-second relational database system. The other three contributions improve components in the architecture of database management systems to increase the query performance and the manageability of descriptive schema-comes-second relational database systems. We are confident that these four contributions can help paving the way to a more flexible future for relational database management technology

Flexibility in Data Management

With the ongoing expansion of information technology, new fields of application requiring data management emerge virtually every day. In our knowledge culture increasing amounts of data and work force organized in more creativity-oriented ways also radically change traditional fields of application and question established assumptions about data management. For instance, investigative analytics and agile software development move towards a very agile and flexible handling of data. As the primary facilitators of data management, database systems have to reflect and support these developments. However, traditional database management technology, in particular relational database systems, is built on assumptions of relatively stable application domains. The need to model all data up front in a prescriptive database schema earned relational database management systems the reputation among developers of being inflexible, dated, and cumbersome to work with. Nevertheless, relational systems still dominate the database market. They are a proven, standardized, and interoperable technology, well-known in IT departments with a work force of experienced and trained developers and administrators. This thesis aims at resolving the growing contradiction between the popularity and omnipresence of relational systems in companies and their increasingly bad reputation among developers. It adapts relational database technology towards more agility and flexibility. We envision a descriptive schema-comes-second relational database system, which is entity-oriented instead of schema-oriented; descriptive rather than prescriptive. The thesis provides four main contributions: (1)~a flexible relational data model, which frees relational data management from having a prescriptive schema; (2)~autonomous physical entity domains, which partition self-descriptive data according to their schema properties for better query performance; (3)~a freely adjustable storage engine, which allows adapting the physical data layout used to properties of the data and of the workload; and (4)~a self-managed indexing infrastructure, which autonomously collects and adapts index information under the presence of dynamic workloads and evolving schemas. The flexible relational data model is the thesis\' central contribution. It describes the functional appearance of the descriptive schema-comes-second relational database system. The other three contributions improve components in the architecture of database management systems to increase the query performance and the manageability of descriptive schema-comes-second relational database systems. We are confident that these four contributions can help paving the way to a more flexible future for relational database management technology

Semi-automatic Database Design for Neuroscience Experiment Management Systems

Neuroinformatics provides tools for neuroscience researchers to study brain function. In order to handle experiment paradigms that change frequently, we are developing a semiautomatic database design tool that will enable an experiment management system (EMS) to manage data with flexibility while retaining the efficiency of a relational database

Development of a pilot data management infrastructure for biomedical researchers at University of Manchester – approach, findings, challenges and outlook of the MaDAM Project

Management and curation of digital data has been becoming ever more important in a higher education and research environment characterised by large and complex data, demand for more interdisciplinary and collaborative work, extended funder requirements and use of e-infrastructures to facilitate new research methods and paradigms. This paper presents the approach, technical infrastructure, findings, challenges and outlook (including future development within the successor project, MiSS) of the ‘MaDAM: Pilot data management infrastructure for biomedical researchers at University of Manchester’ project funded under the infrastructure strand of the JISC Managing Research Data (JISCMRD) programme. MaDAM developed a pilot research data management solution at the University of Manchester based on biomedical researchers’ requirements, which includes technical and governance components with the flexibility to meet future needs across multiple research groups and disciplines

The Effect of Supply Chain Management Practices on Strategic Flexibility: Applied Study on the Jordanian Manufacturing Companies

The purpose of this study is to identify the impact of supply chain management practices on the strategic flexibility of Jordanian manufacturing companies listed in Amman stock exchange and working in international markets, which amount (47) companies. The sample of study composed of (93) managers working in the target companies. In order to achieve the study objectives, the researcher designed a questionnaire consisting of (32) paragraph to collect the required data from study sample. The multiple regression analysis was used to testing the hypotheses. Empirical results found that the supply chain management practices has a positive impact on strategic flexibility, and the highest impact was for the relationship with customers, while the lowest impact was for the quality of information sharing. Also the study results found that the information sharing level has the highest impact on market flexibility and the strategic partnership with supplier has the highest impact on production flexibility, while the relationship with customers has the highest impact on competitive flexibility. Keywords: Supply Chain Management, Supply Chain Management Practices, Strategic Flexibility, Market Flexibility, Production Flexibility, Competitive Flexibility

Secure Cloud Storage: A Framework for Data Protection as a Service in the Multi-cloud Environment

This paper introduces Secure Cloud Storage (SCS), a framework for Data Protection as a Service (DPaaS) to cloud computing users. Compared to the existing Data Encryption as a Service (DEaaS) such as those provided by Amazon and Google, DPaaS provides more flexibility to protect data in the cloud. In addition to supporting the basic data encryption capability as DEaaS does, DPaaS allows users to define fine-grained access control policies to protect their data. Once data is put under an access control policy, it is automatically encrypted and only if the policy is satisfied, the data could be decrypted and accessed by either the data owner or anyone else specified in the policy. The key idea of the SCS framework is to separate data management from security management in addition to defining a full cycle of data security automation from encryption to decryption. As a proof-of-concept for the design, we implemented a prototype of the SCS framework that works with both BT Cloud Compute platform and Amazon EC2. Experiments on the prototype have proved the efficiency of the SCS framework

Managing Flexibility in Distributed Information Systems Architectures

Conktw~tivepressures are forcing organizations to be agile and flexible. Response to changing environmental conditions is an important factorindeterminingcorporateperformance. Organizationalflexibilityisinturncloselyrelatedtothemanagementofinformation systems. Flexibility of information systems needs to be actively managed. Information systems in organizations have evolved from being a function of a centralized MIS department to a function dependent on a distributed collection of information systems resources (hardware,software,andpeople). ThisdistributedcollectionofresourceshasbeenreferredtoastheDistributedInformationSystems Architecture. Managing flexibility in distributed information systems architectures is an interesting and challenging problem. The importance of flexibilityboth at the organizational and information systems levels has been recognized by research in management (Bahrami 1992), operations management (Sethi 1990), and MIS (Lacity,Willcocks and Feeny 1995). However, flexibility in distributed informationsystemarchitectureshasnotbeenexaminedindetail. Flexibilityindistributedinformationsystemarchitecturescanbe of several types. Each type of flexibilityhas distinct characteristics. Understanding different types of flexibility in the context of distributedinformationsystemarchitectures isthereforeextremelyimportant. The operations management literature on flexibility is used as a starting point to categorize various types of flexibility. Analysis of secondary data using grounded theory(Strauss and Corbin 1990) is employed to identifyseveral different types of flexibility. Secondary data used consists of articles on outsourcing and management of information systems from academic publications as well as from practitioner publications such as Datamation,InformationWeek,etc. A taxonomyfor understanding flexibilityin distributed information system architectures is developed. Iherelationshipbeaweenvarioustypesofflexibilityanddifferentinformationsystemsfunctionsisexplored. Thistaxonomyofflexibility, as well as the relationship between types of flexibility, information system functions, and resource requirements has important implications for evaluation of outsourcing decisions

Achieving Strategic Flexibility in the Era of Big Data: The Importance of Knowledge Management and Ambidexterity

Purpose – This research unpacks the micro-mechanisms that exist between an organisation’s ability to conduct Big Data Analytics (BDA) and its achievement of strategic flexibility. Knowledge management capabilities and organisational ambidexterity have long been considered factors influencing the aforementioned relationship. In order to assess this, the authors build on dynamic capabilities as the main theoretical lens through which to examine. Design/methodology/approach – Structural Equation Modelling (SEM) is the main methodological approach used in this research. A structural model was developed and tested based on 215 survey responses collected from managers of organisations in continental Europe. Findings – The results indicate that BDA capabilities are a significant antecedent of an organisation’s strategic flexibility. This relationship, however, is influenced by knowledge management capabilities and ambidexterity. Practical implications – Managers wishing to properly exploit the potential of big data should invest in the elaboration of knowledge management processes across their organisation. This strategy can foster strategic flexibility. Originality/value – Previous research has explored the theoretical links between big data, knowledge management, and strategic flexibility. However, little attention has been paid to the quantitative investigation of the phenomenon