This article comprehensively surveys Arabic Online Handwriting Recognition (AOHR). We address the challenges posed by online handwriting recognition including ligatures, dots and diacritic problems, online/offline touching of text, and geometric variations. Then, we present a general model of AOHR system that incorporates the different phases of an AOHR system. We summarize the main AOHR databases and identify their uses and limitations. Preprocessing techniques that are used in AOHR, viz. normalization, smoothing, de-hooking, baseline identification, and delayed stroke processing, are presented with illustrative examples. We discuss different techniques for Arabic online handwriting segmentation at the character and morpheme levels and identify their limitations. Feature extraction techniques that are used in AOHR are discussed and their challenges identified. We address the classification techniques of non-cursive (characters and digits) and cursive Arabic online handwriting and analyze their applications. We discuss different classification techniques, viz. structural approaches, SVM, Fuzzy SVM, Neural Networks, HMM, Genetic algorithms, decision trees, and rule-based systems, and analyze their performance. Post-processing techniques are also discussed. Several tables that summarize the surveyed publications are provided for ease of reference and comparison. In the conclusions, we summarize the current limitations and difficulties of AOHR, and future directions of research.
The twenty-first century has ushered in the age of data economy, in which data DNA becomes an intrinsic constituent of all data-based organisms and carries important knowledge and insights. An appropriate understanding of data DNA and their organisms relies on a new field: data science and its keystone analytics. Although it is widely debated whether big data is a hype and buzz and data science is at its very early phase, significant challenges and opportunities are emerging or inspired from the research, innovation, business and education of data science and analytics. This paper provides a comprehensive survey and tutorial of fundamental aspects of data science and analytics: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in data economy, profession and competency of data education, and typical pitfalls in data science. This article serves as the first in the field to draw a comprehensive big picture, in addition to rich observations, lessons and thinking about data science and analytics.
An enormous amount of research has been conducted in the area of positioning systems and thus it calls for a detailed literature review of recent localization systems. This paper focuses on recent developments of non-Global Positioning System (GPS) localization/positioning systems. We have presented a new hierarchical method to classify various positioning systems. A comprehensive performance comparison of the techniques and technologies against multiple performance metrics along with the limitations is presented. A few indoor positioning systems have emerged as more successful in particular application environments than others, which are presented at the end.
The aim of this article is to provide an understanding of social networks as a useful addition to the standard tool-box of techniques used by system designers. To this end, we give examples of how data about social links have been collected and used in different application contexts. We develop a broad taxonomy-based overview of common properties of social networks, review how they might be used in different applications, and point out potential pitfalls where appropriate. We propose a framework, distinguishing between two main types of social network-based user selection personalised user selection which identifies target users who may be relevant for a given source node, using the social network around the source as a context, and generic user selection or group delimitation, which filters for a set of users who satisfy a set of application requirements based on their social properties. Using this framework, we survey applications of social networks in three typical kinds of application scenarios: recommender systems, content-sharing systems (e.g., P2P or video streaming), and systems which defend against users who abuse the system (e.g., spam or sybil attacks). In each case, we discuss potential directions for future research that involve using social network properties.
Technological advances allow more physical objects to connect to the Internet and provide their services on the Web as resources. Search engines are the key to fully utilize this emerging Web of Things, as they bridge users and applications with resources needed for their operation. Developing these systems is challenging due to the diversity of Web of Things resources that they work with. Each combination of resources in query resolution process requires a different type of search engine with its own technical challenges and usage scenarios. This diversity complicates both the development of new systems and assessment of the state of the art. In this article, we present a systematic survey on Web of Things Search Engines (WoTSE), focusing on the diversity in forms of these systems. We collect and analyze over 200 related academic works to build a flexible conceptual model for WoTSE. We develop an analytical framework on this model to review the development of the field and its current status, reflected by 30 representative works in the area. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and an ideal WoTSE.
Locality of information is a major concern for the design of distributed algorithms. With the LOCAL model, theoretical research already established a common model of locality that has gained little practical relevance. As a result, practical research de facto lacks any common locality model. The only common denominator among practitioners is that a local algorithm is distributed with a limited scope of interaction. This paper closes the gap by introducing four practically motivated classes of locality that successively weaken the strict requirements of the LOCAL model. These classes are applied to categorize and to survey 32 local algorithms from nine different application domains. A detailed comparison shows the practicality of the classification and provides interesting insights. For example, the majority of algorithms limit the scope of interaction to at most two hops, independent of their locality class. Moreover, the application domain of algorithms tends to influence their degree of locality.
Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms - disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the applications in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. This paper presents a detailed discussion of the state-of-the-art metrics used for the detection and the evaluation of community structure. Finally, experiments are conducted on synthetic and real networks to present a comparative analysis of these metrics in measuring the goodness of the detected community structure.