Periodic Pattern Mining a Algorithms and Applications

Owing to a large number of applications periodic pattern mining has been extensively studied for over a decade Periodic pattern is a pattern that repeats itself with a specific period in a give sequence Periodic patterns can be mined from datasets like biological sequences continuous and discrete time series data spatiotemporal data and social networks Periodic patterns are classified based on different criteria Periodic patterns are categorized as frequent periodic patterns and statistically significant patterns based on the frequency of occurrence Frequent periodic patterns are in turn classified as perfect and imperfect periodic patterns full and partial periodic patterns synchronous and asynchronous periodic patterns dense periodic patterns approximate periodic patterns This paper presents a survey of the state of art research on periodic pattern mining algorithms and their application areas A discussion of merits and demerits of these algorithms was given The paper also presents a brief overview of algorithms that can be applied for specific types of datasets like spatiotemporal data and social network

The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap

This paper presents an ecosystem for personal knowledge graphs (PKG), commonly defined as resources of structured information about entities related to an individual, their attributes, and the relations between them. PKGs are a key enabler of secure and sophisticated personal data management and personalized services. However, there are challenges that need to be addressed before PKGs can achieve widespread adoption. One of the fundamental challenges is the very definition of what constitutes a PKG, as there are multiple interpretations of the term. We propose our own definition of a PKG, emphasizing the aspects of (1) data ownership by a single individual and (2) the delivery of personalized services as the primary purpose. We further argue that a holistic view of PKGs is needed to unlock their full potential, and propose a unified framework for PKGs, where the PKG is a part of a larger ecosystem with clear interfaces towards data services and data sources. A comprehensive survey and synthesis of existing work is conducted, with a mapping of the surveyed work into the proposed unified ecosystem. Finally, we identify open challenges and research opportunities for the ecosystem as a whole, as well as for the specific aspects of PKGs, which include population, representation and management, and utilization

Enriching e-learning metadata through digital library usage analysis

Purpose: In this paper we propose an evaluation framework for analyzing learning objects usage, with the aim of extracting useful information for improving the quality of the metadata used to describe the learning objects, but also for personalization purposes, including user models and adaptive itineraries. Methodology: We present experimental results from the log usage analysis during one academic semester of two different subjects, 350 students. The experiment looks into raw server log data generated from the interactions of the students with the classroom learning objects, in order to find relevant information that can be used to improve the metadata used for describing both the learning objects and the learning process. Findings: Preliminary studies have been carried out in order to obtain an initial picture of the interactions between learners and the virtual campus, including both services and resources usage. These studies try to establish elationships between user profiles and their information and navigational behavior in the virtual campus, with the aim of promoting personalization and improving the understanding of what learning in virtual environments means. Research limitations: During the formal learning process, students use learning resources from the virtual classroom provided by the academic library, but they also search for information outside the virtual campus. Not all of these usage data are considered in the model we propose. Further research needs to be done in order to get a complete view of the information search behavior of students for improving the users’ profile and creating better personalized services. Practical implications: In this paper we suggest how a selection of fields used in the LOM standard could be used for enriching the description of learning objects, automatically in some cases, from the learning objects usage performed by an academic community. Originality: Ever since the beginnings of libraries, they have been a “quiet storage place”. With the development of digital libraries, they become a meeting place where explicit and implicit recommendations about information sources can be shared among users. Social and learning process interactions, therefore, can be considered another knowledge source

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications