In recent years, versioning of business data has become increasingly important in enterprise solutions. In the context of big data, where the 5Vs (Volume, Velocity, Variety, Value, and Veracity) play a pivotal role, the management of data versioning gains even greater significance. As enterprises grapple with massive volumes of data generated at varying velocities and exhibiting diverse formats, ensuring the accuracy, completeness, and consistency of data throughout its lifecycle becomes paramount. System of record solutions, which cover most enterprise solutions, require the management of data life history in both system and business time. This means that information must be stored in a way that covers past, present, and future states, such as a contract with a start date in the past and an end date in the future that may require correction at any point during its lifetime. While some systems offer transaction time rollback features, they do not address the business life history dimension of a contract or asset, which requires the developer to code the business rules of the requirements. The relational data model is unable to inherently use relational constraints where a business time dimension of the data is required, as it is a “current view” and not designed for this purpose. Therefore, there is a need for better autonomous capabilities for version control of data, which will bring new functionality and cost reduction in application development and maintenance, reduce coding complexity, and increase productivity. This paper presents an approach to relational data management that relieves the developer from the need to code the business rules for versioning. The framework, called Ld8a, works with a standard Oracle database that keeps the developer in the “current view” paradigm but allows them to specify the point in time that logical insert, update, and delete events take place with the infrastructure autonomously maintaining relational correctness of the dataset across time. The Ld8a framework has been used to address the vehicle registration scenario used by AWS in presenting the capabilities of the Quantum Ledger Database product. This approach offers a solution that maintains referential integrity by the infrastructure across time, making version control of data easier and more efficient for developers.

The rapid growth of social media networks has resulted in the generation of a vast data amount, making it impractical to conduct manual analyses to extract newsworthy events. Thus, automated event detection mechanisms are invaluable to the community. However, a clear majority of the available approaches rely only on data statistics without considering linguistics. A few approaches involved linguistics, only to extract textual event details without the corresponding temporal details. Since linguistics define words’ structure and meaning, a severe information loss can happen without considering them. Targeting this limitation, we propose a novel method named WhatsUp to detect temporal and fine-grained textual event details, using linguistics captured by self-learned word embeddings and their hierarchical relationships and statistics captured by frequency-based measures. We evaluate our approach on recent social media data from two diverse domains and compare the performance with several state-of-the-art methods. Evaluations cover temporal and textual event aspects, and results show that WhatsUp notably outperforms state-of-the-art methods. We also analyse the efficiency, revealing that WhatsUp is sufficiently fast for (near) real-time detection. Further, the usage of unsupervised learning techniques, including self-learned embedding, makes our approach expandable to any language, platform and domain and provides capabilities to understand data-specific linguistics.

Abstract Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termedEmbed2Detectfor event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings givesEmbed2Detectthe capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show thatEmbed2Detectis capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

This paper is an extended abstract for work [1]. We propose a novel method termed Embed2Detect for event detection in social media by mainly combining the characteristics in word embeddings and dendrograms. The adoption of word embeddings incorporates powerful semantical features into event detection to overcome a major limitation inherent in previous approaches.

Research in analysis of big scholarly data has increased in the recent past and it aims to understand research dynamics and forecast research trends. The ultimate objective in this research is to design and implement novel and scalable methods for extracting knowledge and computational history. While citations are highly used to identify emerging/rising research topics, they can take months or even years to stabilise enough to reveal research trends. Consequently, it is necessary to develop faster yet accurate methods for trend analysis and computational history that dig into content and semantics of an article. Therefore, this paper aims to conduct a fine-grained content analysis of scientific corpora from the domain of Machine Learning. This analysis uses DeepHist, a deep learning-based computational history approach; the approach relies on a dynamic word embedding that aims to represent words with low-dimensional vectors computed by deep neural networks. The scientific corpora come from 5991 publications from Neural Information Processing Systems (NIPS) conference between 1987 and 2015 which are divided into six 5-year timespans. The analysis of these corpora generates visualisations produced by applying t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction. The qualitative and quantitative study reported here reveals the evolution of the prominent Machine Learning keywords; this evolution supports the popularity of current research topics in the field. This support is evident given how well the popularity of the detected keywords correlates with the citation counts received by their corresponding papers: Spearman's positive correlation is 100%. With such a strong result, this work evidences the utility of deep learning techniques for determining the computational history of science.

Cloud computing is fast becoming an integral part of the Information Technology (IT) industry and looks set to only strengthen its share of the market. This paper discusses the different types of cloud computing technology and discusses the results of our research survey which was designed to examine the obstacles preventing organizations from adopting cloud (with a particular focus on the security issues). Future work will include the development and testing of an e-learning tool using virtualization, web services and open source platforms to assess the feasibility of adopting cloud technology with minimal security fears. The work will produce recommendations to organizations wishing to adopt cloud technology. Finally it will evaluate the effectiveness of the Cloud in the learning environment.

The smartphone market and its relative technology is expanding rapidly. Due to smartphone's rich user interface and growing numbers, smartphones are becoming ubiquitous workplace devices for many prime priority applications such Business Intelligence (BI). This paper reviews the literature on recent developments in Mobile BI, conducts a comparison of development platforms, mobile frameworks and data storage strategies. Future market trends are also discussed. The paper describes the process for designing a mobile BI application prototype and evaluates the mechanism behind the 'App' BI operation and its functionalities. Finally, the paper provides guidelines for developing Mobile BI applications.

Intelligent mobile advertising is an important area for business growth. The foundation of this growth is largely dependent on the semantic web that relies on ontologies to provide various taxonomies. The Intelligent Mobile Advertising Service (iMAS) ontology is developed from its roots in marketing, enabling the development of the Mobile Semantic Advertising Model (MSAM). However, the dynamic nature of marketing in today's world of the web has led to the work done on the iMAS location based service. The prototype was tested and the results were applied to the development of MSAM ontology. This paper introduces a formal approach to the iMAS model ontology. An overview of retail and marketing ontologies and the iMAS model ontology is discussed. The final section covers challenges and future work.