Current developments in standardization activities for database management systems include the development of the next versions of the Structured Query Language (SQL), and related standards for labelled property graphs. This short paper explores mechanisms for unified implementation of record-based relational and property graph databases: its contribution is that modeling SQL foreign keys as reference keys facilitates the graph insert and advanced pattern matching mechanisms of Property Graph Database Management Systems (PGDMS) and allows the construction of multiple type graph models on a single relational database.

A data management server that combines the relational data model found in traditional databases with the graphical models being developed by newer data products can provide its knowledge model to generative tools for large language models. Both models have been reconciled at the implementation level by replacing foreign keys by reference values. This boosts performance and allows effectively following links instead of lookup joins. The reference value will never change as it uses the record address in the log file. This paper reports on progress in providing such a synthesis and includes an example of a knowledge base that is triple-based and would also be supported in such a system.

The International Standards Organization (ISO) is developing a new standard for Graph Query Language, with a particular focus on graph patterns with repeating paths. The Linked Database Benchmark Council (LDBC) has developed benchmarks to test proposed implementations. Their Financial Benchmark includes a novel requirement for truncation of results. This paper presents an open-source implementation of the benchmark workloads and truncation.

This paper reviews the changes for database technology represented by the current development of the draft international standard ISO 39075 (Database Languages - GQL), which seeks a unified specification for property graphs and knowledge graphs. This paper examines these current developments as part of our review of the evolution of database technology, and their relation to the longer-term goal of supporting the Semantic Web using relational technology.

This paper reviews suggestions for changes to database technology coming from the work of many researchers, particularly those working with evolving big data. We discuss new approaches to remote data access and standards that better provide for durability and auditability in settings including business and scientific computing. We propose ways in which the language standards could evolve, with proof-of-concept implementations on Github.

In the knowledge economy, sharing the key resource-knowledge - is a key challenge. A virtual community (VC), which is a socio-technological system, is considered as a suitable tool to meet the challenge. However, many issues regarding sharing knowledge through VCs need to be addressed. One of such issues is organisational culture. The paper aims to conceptualise the relationship between knowledge sharing and organisational culture in an online environment like VCs. The research uses the organisational cultural model developed by Khan and Usoro. The model consists of support, coordination, innovation and rules orientations. The research hypothesised that support, coordination and innovation orientations have positive relationships with knowledge sharing in online environments while rules orientation has a negative relationship. The research is a conceptual study and based on literature study. In future studies, data will be collected from VCs to test the model.

Data Integration of heterogeneous data sources relies either on periodically transferring large amounts of data to a physical Data Warehouse or retrieving data from the sources on request only. The latter results in the creation of what is referred to as a virtual Data Warehouse, which is preferable when the use of the latest data is paramount. However, the downside is that it adds network traffic and suffers from performance degradation when the amount of data is high. In this paper, we propose the use of a readCheck validator to ensure the timeliness of the queried data and reduced data traffic. It is further shown that the readCheck allows transactions to update data in the data sources obeying full Atomicity, Consistency, Isolation, and Durability (ACID) properties.

Schema and data integration have been a challenge for more than 40 years. While data warehouse technologies are quite a success story, there is still a lack of information integration methods, especially if the data sources are based on different data models or do not have a schema. Enterprise Information Integration has to deal with heterogeneous data sources and requires up-to-date high-quality information to provide a reliable basis for analysis and decision making. The paper proposes virtual integration using the Typed Graph Model to support schema mediation. The integration process first converts the structure of each source into a typed graph schema, which is then matched to the mediated schema. Mapping rules define transformations between the schemata to reconcile semantics. The mapping can be visually validated by experts. It provides indicators and rules to achieve a consistent schema mapping, which leads to high data integrity and quality.

Many knowledge sharing models have been developed but they mostly explain how knowledge flows from source to destination and hardly provide adequately validated organisational cultural factors that affect knowledge sharing in virtual communities (VCs) though VCs are recognised in literature as a software tool for knowledge sharing in organisations. The research reported in this paper undertook this task by developing a conceptual model to hypothesise the relationship between organisational culture (support, innovation, co-ordination and rules orientations) and knowledge sharing. Data was collected from over 200 VC members. Detailed group analyses were performed to test the conceptual model. The research found that support, co-ordination and rules orientation have positive causal relationships with sharing knowledge online. The research also highlights that just a software tool like a VC will not be sufficient to enable knowledge sharing online. The implication is that training, support in personal and work problems, inter-departmental co-ordination and teamwork will have positive affect on knowledge sharing in VCs. Also, face-to-face gatherings and a leading role of managers to share their knowledge will increase online knowledge sharing. The research has implications for KM (Knowledge Management) practitioners, managers, and information systems researchers. The findings of the research can be used in assessing VC knowledge sharing systems plans from an organisational cultural perspective.

At DBKDA 2019, we demonstrated that StrongDBMS with simple but rigorous optimistic algorithms, provides better performance in situations of high concurrency than major commercial database management systems (DBMS). The demonstration was convincing but the reasons for its success were not fully analysed. There is a brief account of the results below. In this short contribution, we wish to discuss the reasons for the results. The analysis leads to a strong criticism of all DBMS algorithms based on locking, and based on these results, it is not fanciful to suggest that it is time to re-engineer existing DBMS.

Big data development is a disturbing issue that will affect enterprise across various sectors. The increase of data volume, high speed of data generation and increasing rate of different data from heterogeneous sources have led to difficulties in data management. This paper first reviews different aspects of big data management, including data integration and traditional data warehouse, and their associated challenges. The problems include increase of redundant data, data accessibility, time consumption in data modelling and data movement from heterogeneous sources into a central database, especially in the big data environment. We then propose a logical data management approach using RESTview technology to integrate and analyse data, without fully adopting traditional ETL processes. Data that for governance, corporate, security or other restriction reasons cannot be copied or moved, can easily be accessed, integrated and analysed, without creating a central repository. Data can be kept in its original form and location, eliminating the movement of data, significantly speeding up the process and allowing for live data interrogation. It may not be the practical solution for every situation but, it is a feasible solution that is comparably cost effective.

Purpose – The purpose of this paper is to show how description logics (DLs) can be applied to formalizing the information bearing capability (IBC) of paths in entity-relationship (ER) schemata. Design/methodology/approach – The approach follows and extends the idea presented in Xu and Feng (2004), which applies DLs to classifying paths in an ER schema. To verify whether the information content of a data construct (e.g. a path) covers a semantic relation (which formulates a piece of information requirement), the principle of IBC under the source-bearer-receiver framework is presented. It is observed that the IBC principle can be formalized by constructing DL expressions and examining constructors (e.g. quantifiers). Findings – Description logic can be used as a tool to describe the meanings represented by paths in an ER schema and formalize their IBC. The criteria for identifying data construct distinguishability are also discovered by examining quantifiers in DL expressions of paths of an ER schema. Originality/value – This paper focuses on classifying paths in data schemas and verifying their formalized IBC by using DLs and the IBC principle. It is a new point of view for evaluation of data representation, which looks at the information borne by data but not data dependencies.

This paper presents an approach to Reinforcement Learning that seems to work very well in changing environments. The experiments are based on an unmanned vehicle problem where the vehicle is equipped with navigation cameras and uses a multilayer perceptron (MLP). The route can change and obstacles can be added without warning. In the steady state, no learning takes place, but the system maintains a small cache of recent inputs and rewards. When a negative reward occurs, learning restarts, based not on the immediate situation but on the memory that has generated the greatest error, and the updated strategy is quickly reviewed using the cache of recent memories within an accelerated learning phase. In the resulting Reluctant Learning algorithm the multiple use of a small quantity of previous experiences to validate updates to the strategy moves the MLP towards convergence and finds a balance between exploration of improvements to strategy and exploitation of previous learning.

Affiliate Networks are the main source of communication between publishers and advertisers where publishers normally subscribe as a service provider and advertisers as an employer. These networks are helping both the publishers and advertisers in terms of providing them with a platform where they can build an automated affiliate connection with each other via these affiliate networks. The problem that is highlighted in this paper is the huge gap that exists between the publisher and advertiser in these affiliate networks and a solution is provided by proposing a priority recommendation system based on K-Means clustering algorithm. Every advertiser desires to have that type of publisher who is already practiced in his category of business or at least has the same skills and talent. This paper presents the concept of a recommendation system based on clustering the real-time data of all the existing transactions of publishers and advertisers of an affiliate network and based on the resulting POST-HOC classified data, a new publisher or advertiser will automatically be classified. Real-time data is provided by Affiliate Future a well-known company among all the affiliate networks. After carefully examining the data the most effective attribute is selected as the base attribute for clustering. The data is encoded into binary numbers for the purpose of clustering. More than one distance approaches are used and the most suitable one is selected for classifying the data.

This paper develops a new theoretical model to represent the important knowledge sharing factors and their role in development of trust in professional learning organisations. These factors are built upon the assumptions of organisations knowledge sharing behaviour and the role of technological advancement/awareness based on literature. The factors we have introduced are hard and soft ties, habits and their relationships to tie's (soft/hard), development of personal mastery, development of system thinking, internal dialogue with the use of technology, and resource exchange (physical and explicit). We have also argued that some factors such as age, gender, citizenship, and marital status are not the significant factors in building trust for knowledge sharing behaviour in professional real organisations environments.

We consider means of extracting information from two data streams simultaneously when each data stream contains information about the other, i.e., there is redundancy in the data streams and we wish to identify the commonality between the data streams. The standard statistical method for doing this is canonical correlation analysis and so we consider extensions of this method: in the first group we use Bregman divergences to create methods of extracting information from the dual data streams which are optimal when the data has a distribution other than the Gaussian distribution. In the second advance, we use the method of reservoir computing in order to extract non-linear relationships. Finally we join the two methods and illustrate on a database of student marks.

Curvilinear Component Analysis (CCA) is a useful data visualisation method. CCA has the technical property that its optimisation surface, as defined by its stress function, changes during the optimisation according to a decreasing parameter. CCA uses a variant of the stochastic gradient descent method to create a mapping of data. In the optimisation method of CCA, the stress function is only a general guide towards an acceptable mapping. In other multidimensional scaling methods such as Sammon’s mapping, the best mapping among multiple runs from different initialisations can be chosen by selecting the mapping with the lowest stress, whereas in CCA the embedding is simply the result of one run, surely we can have multiple starts. As a consequence of the absence of an objective function to be used as a selection criterion, embedding made by CCA can be poorly optimised. In this paper we present a new way of improving the optimisation of CCA by integrating non-stress data visualisation quality measures into the existing algorithm. We first use data visualisation quality measures to select the best mapping from multiple runs of a standard stochastic gradient descent implementation; then we tune various parameters involved to achieve further enhancement. A brief comparison with other dimensionality reduction methods is included.