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Big data technologies are becoming widely used, not only for recording but also for analyzing human generated data. Indeed, the recent development in systems and technologies provided many areas with data processing technologies to extract meaningful information from massive amounts of data. A good example of areas where the generated data is voluminous is eLearning, e.g. Massive Open Online Courses...
We are now witnessing an unprecedented growth ofdata that needs to be processed at always increasing rates inorder to extract valuable insights. Big Data streaming analyticstools have been developed to cope with the online dimensionof data processing: they enable real-time handling of live datasources by means of stateful aggregations (operators). Currentstate-of-art frameworks (e.g. Apache Flink...
This paper proposes a novel approach to book recommendation: we utilise big data created by thousands of book social cataloguing website users and treat it as a collectively written meta-annotation of a book. After learning semantic similarity between a large collection of books by applying algorithms of natural language processing (probabilistic topic models and semantic neural networks) to the symbolic...
Collaborative Filtering (CF) is widely used in large-scale recommendation engines because of its efficiency, accuracy and scalability. However, in practice, the fact that recommendation engines based on CF require interactions between users and items before making recommendations, make it inappropriate for new items which haven't been exposed to the end users to interact with. This is known as the...
The problem being addressed in this paper is that using brute force in Natural Language Processing and Machine Learning combined with advanced statistics will only approximate meaning and thus will not deliver in terms of real text understanding. Counting words and tracking word order or parsing by syntax will also result in probability and guesswork at best. Their vendors struggle in delivering accurate...
With the rapid advances in sensor data collection and communication, heterogeneous and real-time IOT data is increasing rapidly. To make full use of the information, lots of web technologies are introduced to integrate the IOT world and Web world into an unified Web of Things(WOT). While most of the existing efforts are mainly focused on the modeling, annotation, and representation of WOT data, there...
With the coming of the era of big data, it is most urgent to establish the knowledge computational engine for the purpose of discovering implicit and valuable knowledge from the huge, rapidly dynamic, and complex network data. In this paper, we first survey the mainstream knowledge computational engines from four aspects and point out their deficiency. To cover these shortages, we propose the open...
Analytics-as-a-Service (AaaS) has become indispensable because it affords stakeholders to discover knowledge in Big Data. Previously, data stored in data warehouses follow some schema and standardization which leads to efficient data mining. However, the "Big Data" epoch has witnessed the rise of structured, semi-structured, and unstructured data, a trend that motivated enterprises to employ...
Reasoning is one of the essential application areas of the modern Semantic Web. Nowadays, the semantic reasoning algorithms are facing significant challenges when dealing with the emergence of the Internet-scale knowledge bases, comprising extremely large amounts of data. The traditional reasoning approaches have only been approved for small, closed, trustworthy, consistent, coherent and static data...
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