Big Data has gained attention due to its significant impact on society. With the growth of IoT, data has grown exponentially. Big Data has played an essential role in decision-making. After collecting a large volume of data, one of the critical tasks is performing analysis. As a result, there is a rise in the adoption of data visualization, where the raw data is converted into useful information through research and the generation of patterns. The representation of the natural numbers offers an easier way to interpret the collected data, and thus, it becomes easier to comprehend the significance of the data collected. While data visualization is significant, it faces numerous challenges; therefore, this paper aims to provide the different challenges of Big Data visualization.

IoT generates a large volume of data in different formats. Exponential data generation has resulted in challenges in capturing, managing, storing, analyzing, and visualizing data. This necessitates the implementation of exemplary database architecture to store data. The paper aims to evaluate the various database architectures.

There is a surge in the usage of IoT platforms, which has increased the number of interconnected devices worldwide. There are different challenges faced with adopting the IoT platform, and this paper will evaluate the storage challenges.

A polystore system is composed of multiple heterogeneous databases which are used to store data that best suites its features for faster processing and querying. A single database does not fit all data formats, which has resulted in the development of a polystore system that leverages databases belonging to different categories such as relational, NoSQL, and NewSQL. Retrieving data from relational databases for analytics is a costly operation. This paper presents an extension to a polystore system which queries data from different relational databases using a standard in-memory data format for analytical queries with low latency.

Big Data analytics is used in decision making. It involves heavy computation to obtain exact answers. To alleviate this problem, approximate query processing (AQP) was adopted, which provides approximate results with error bounds. The AQP models which have been proposed are supported only by a single database. In an organization, big data is stored in multiple databases that have different data models. This research aims to provide AQP as a middleware solution using query optimization for heterogeneous databases.

As the world continues to encounter challenges related to public health and economic well-being, technology and big data can be used to carry out analyses and provide predictions concerning the progression of the COVID-19 pandemic. This information allows virologists and epidemiologists to understand the Coronavirus better. Historically, medical experts relied on hospital records and medical publications to manage pandemics. However, this approach proved to be slow and inefficient when it comes to predicting a disease's course. Using contemporary technologies for collecting and analyzing information is a fast and efficient way.

As the number of databases continues to grow data scientists need to use data from different sources to run machine learning algorithms for analysis. Data science results depend upon the quality of data been extracted. The objective of this research paper is to implement a federated query processing framework which extracts data from different data sources and stores the result datasets in a common in-memory data format. This helps data scientists to perform their analysis and execute machine learning algorithms using different data engines without having to convert the data into their native data format and improve the performance.

Summary This paper develops algorithms and investigates various classifiers to determine the authenticity of short social network postings, an average of 20.6 words, from Facebook. This paper presents and discusses several experiments using a variety of classifiers. The goal of this research is to determine the degree to which such postings can be authenticated as coming from the purported user and not from an intruder. Various sets of stylometry and ad hoc social networking features were developed to categorize 9259 posts from 30 Facebook authors as authentic or non‐authentic. An algorithm to utilize machine‐learning classifiers for investigating this problem is described, and an additional voting algorithm that combines three classifiers is investigated. This research is one of the first works that focused on authorship authentication in short messages, such as postings on social network sites. The challenges of applying traditional stylometry techniques on short messages are discussed. Experimental results demonstrate an average accuracy rate of 79.6% among 30 users. Further empirical analyses evaluate the effect of sample size, feature selection, user writing style, and classification method on authorship authentication, indicating varying degrees of success compared with previous studies. Copyright © 2016 John Wiley & Sons, Ltd.

Biometric systems such as fingerprint, iris, DNA became popular methods in user authentication. Compared to these biometric systems, keystroke biometric authentication systems have not gained so much attention because of lower accuracy compared to other biometric systems. A number of researches have been conducted on keystroke biometric using different generative and discriminative classifiers. As Hidden Markov Models have proven a great success in voice recognition, this study investigates Hidden Markov Models in keystroke dynamic. This paper proposes a novel user verification technique using 1-substate Hidden Markov Model through keystroke dynamic. To verify the effectiveness of the proposed system, extensive experiments have been conducted and 80% accuracy was achieved by the proposed system.

The City of White Plains has a residential population of 57,000 people, which increases every day by 225,000 people who come in from the outlying suburbs to work and shop in the city. Due to the traffic created and the large number of spread-out parking locations, there is an urgent demand of creating an mobile app allowing drivers to find available parking spaces easily. However, the solution specifically focusing on this issue has been rarely investigated by the prior research. In this paper, we represent our case study of a smart city web application using semantic design for a governmental organization's parking app. The app navigates customers to parking structures in conjunction with their destination. The app's user interface was constructed using HTML, CSS, and JavaScript, and a tag-based network structure to facilitate exploratory behavior. The work culminated in a completed user interface, a report detailing its perceived benefits, and an evaluation of its application. Use cases are described for three applications-the Passport app currently employed by the City of White Plains, the app developed in this study, and a future app using Internet of Things (IoT) technology.

The 2008 federal Higher Education Opportunity Act requires institutions of higher learning to make greater access control efforts for the purposes of assuring that students of record are those actually accessing the systems and taking exams in online courses by adopting identification technologies as they become more ubiquitous. To meet these needs, keystroke and stylometry biometrics were investigated towards developing a robust system to authenticate (verify) online test takers. Performance statistics on keystroke, stylometry, and combined keystroke-stylometry systems were obtained on data from 40 test-taking students enrolled in a university course. The best equal-error-rate performance on the keystroke system was 0.5% which is an improvement over earlier reported results on this system. The performance of the stylometry system, however, was rather poor and did not boost the performance of the keystroke system, indicating that stylometry is not suitable for text lengths of short-answer tests unless the features can be substantially improved, at least for the method employed.

A novel keystroke biometric system for long-text input was developed and evaluated for user identification and authentication applications. The system consists of a Java applet to collect raw keystroke data over the Internet, a feature extractor, and pattern classifiers to make identification or authentication decisions. Experiments on more than 100 participants investigated two input modes—copy and free-text—and two keyboard types—desktop and laptop. The system can accurately identify or authenticate individuals if the same type of keyboard is used to produce the enrollment and questioned input samples. Longitudinal experiments quantified performance degradation over intervals of several weeks and two years. Additional experiments investigated the system’s hierarchical model, parameter settings, assumptions, and sufficiency of enrollment samples and input-text length. Although evaluated on input texts up to 650 keystrokes, the authors found that input of 300 keystrokes, roughly four lines of text, is sufficient for the important applications described.

Similarity and dissimilarity measures play an important role in pattern classification and clustering. For a century, researchers have searched for a good measure. Here, we review, categorize, and evaluate various binary vector similarity/dissimilarity measures for character recognition. One of the most contentious disputes in the similarity measure selection problem is whether the measure includes or excludes negative matches. While inner-product based similarity measures consider only positive matches, other conventional measures credit both positive and negative matches equally. Hence, we propose an enhanced similarity measure that gives variable credits and show that it is superior to conventional measures in an offline handwritten character recognition application. Finally, the proposed similarity measure can be further boosted by applying weights and we demonstrate that it outperforms the weighted Hamming distance.

Handwriting originates from a particular copybook style such as Palmer or Zaner-Bloser that one learns in childhood. Since questioned document examination plays an important investigative and forensic role in many types of crime, it is important to develop a system that helps objectively identify a questioned document's handwriting style. We proposed a handwriting analysis system that can assist a document examiner in the identification of the writer's handwriting style and therefore of his/her origin or nationality. We collected 33 English alphabet copybook styles from 18 countries. Here, we extend the analysis using several data mining techniques to discover important information that can be gleaned from a handwriting copybook style image database, e.g., the most information bearing alphabet characters for the purpose of copybook style identification and the relationship between geographical regions and similarity based clusters of copybook styles.

We investigate the detection of handwriting forged by novices. To facilitate document examination it is important to develop an automated system to identify forgeries, or at least to identify those handwritings that are likely to be forged. Because forgers often carefully copy or trace genuine handwriting, we hypothesize that good forgeries — those that retain the shape and size of genuine writing — are usually written more slowly and are therefore wrinklier (less smooth) than genuine writing. From online handwriting samples we find that the writing speed of the good forgeries is significantly slower than that of the genuine writings. From corresponding offline samples we find that the wrinkliness of the good forgeries is significantly greater than that of the genuine writings, showing that this feature can help identify candidate forgeries from scanned documents. Using a total of eight handwriting distance features, including the wrinkliness feature, we train a neural network to achieved 89% accuracy on detecting forged handwriting on test samples from ten writers.

Classifying an unknown input is a fundamental problem in pattern recognition. A common method is to define a distance metric between patterns and find the most similar pattern in the reference set. When patterns are in binary feature vector form, there have been two approaches to improve the performance over the equal-weighted Hamming distance metric. One is to give different weights to different features using an optimization technique, and the other is to use a similarity measure that gives full credit to features present in both patterns and the less credit to those absent from both patterns. Both approaches have been reported to perform better than the na ve Hamming distance approach. In this paper, we propose to combine these two approaches using a genetic algorithm to optimize weights. Experimental results show that this method is superior to conventional measures in an OCR application.

A recognize-then-segment recognizer of unconstrained handprinting uses a unified tablet-display to provide a paper-like computer interface. Whereas most handwriting recognition systems segment and then recognize, this one recognizes and then finds the best segmentation. It classifies strokes, generates character hypotheses, and verifies hypotheses to estimate the optimal character sequence for each word of run-on handwritten characters. Linguistic constraints can limit the choices. The system is implemented on an IBM workstation, accepts run-on characters written on a tablet, and performs recognition in real time.

An on-line character recognizer was improved by making trade-offs on computation speed, recognition accuracy, and flexibility. Firstly, the error rate was halved by increasing computation precision at the expense of speed, achieving a character recognition accuracy of 97.3 percent. Secondly, without loss of accuracy, computation speed was increased by an order of magnitude over the original speed, to 85 characters/second on the IBM System 370/Model 3081. This was done by using a fast linear match to narrow the character choices for a slow but accurate elastic (non-linear) match. Some loss in flexibility resulted, however, because the linear match requires the same number of handwritten strokes for input and prototype characters. Also, the error rate of elastic matching was found to be about half that of linear matching.

A recognize-then-segment recognizer of unconstrained handprinting is described with uses a unified tablet-display to provide a paperlike computer interface. Whereas most handwriting recognition systems segment and then recognize, this one recognizes and then finds the best segmentation. It classifies strokes, generates character hypotheses, and verifies hypotheses to estimate the optimal character sequence for each word of runon handwritten characters. Linguistic constraints can limit the choices. The system is implemented on an IBM workstation, accepts runon characters written on a tablet, and performs recognition in real time.>

This survey describes the state of the art of online handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Online versus offline recognition, digitizer technology, and handwriting properties and recognition problems are discussed. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined.>

The state of the art of online handwriting recognition is surveyed based on an extensive review of the literature. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. It is found that online recognizers, except for bar-code readers and other specialized equipment, are not applicable to machine-printed characters.>

A Markov-model acoustic-phonetic component is constructed for the synthesis of standard acoustic representations of connected speech. The primary building blocks are phones with Markov models structured so that phone length, spectral power and fundamental frequency are parametrically controlled. The model generates acoustic parameter outputs at 10-ms time steps. The acoustic-phonetic component permits matching between actual acoustic data and internally modeled acoustic data, and can be employed in various ways - to automatically label speech, as a phone decoder to obtain estimated phone strings, and in speech recognizers which match at the acoustic level.

In the recognition of complex patterns it is becoming more apparent that segmentation (object isolation) and pattern classification cannot be performed by independent procedures. A pilot study is described which employs feedback to control the interaction of segmentation and segment classification in a system under development for automatic recognition of continuous speech. The potential advantages of employing such interaction in this work are discussed, and data are presented which indicate that this approach is promising.

Two techniques are described for efficient encoding of features in pattern recognition. It is shown that the number of binary digits needed to represent patterns can be greatly reduced using efficient feature encoding. Four-thousand utterances based on a 40-word vocabulary are used to evaluate the techniques.