A strategy for data storage and the search for semi-structured data in the Web

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

Numerous e-news channels publish the daily happenings in the world from different sources. These huge amounts of news articles have lamentably conceived the information overload issue among the users. Hence text mining, which aims in extracting previously unknown information from unstructured text, has been widely used by several researchers to segregate full news articles however, the news headlines categorization is still specifically limited. Therefore, considering this limitation, the current research aims to propose a framework that will self-learn and automatically classify any given news headline into its corresponding news category using artificial intelligence methods i.e. text mining and machine learning algorithms. The proposed framework consists of three stages: Exploratory Data Analysis, Text Pre-processing, and Text Classification. For exploratory data analysis, the top 10 most frequent balanced news categories are chosen so that further processing of data can be done on a more balanced version of the dataset. After exploring the data, text pre-processing techniques are applied to make the data transformed, normalized, and structured. Finally, text classification is carried out with two approaches: unsupervised classification using Mean Shift and K-means algorithms and supervised classification using Logistic Regression with Bag of Words and TF-IDF algorithm. To depict the working of the proposed framework, a case study is presented on a news headlines dataset which accurately performed news headlines classification.

The popularity of web applications and platforms for collecting big data has set the goal of efficient data storage. NoSQL databases have become widely used for data storage and scaling. However, there are also a number of problems associated with the choice of efficient data storage and data schemes. The solution of these problems requires an understanding of the runtime conditions and possible loading of the system, which relies primarily on dedicated computing resources. The same data storage schemes can show different efficiency while under different critical load conditions or different computing infrastructures. In this paper, we propose a framework and experiment methodology for two NoSQL storage schemes that allow us to evaluate the most preferred option in the design of the developed application. The paper presents an analysis of the efficiency of two database schemas for maintaining a reliable saving of semi-structured data stream with specified characteristics.

Many data storage schemes have been proposed in the past for keeping dependable data storage, but they are not designed for Unattended Wireless Sensor Networks (UWSNs). When applying these schemes to UWSNs, most of them have limitations such as high storage requirement, transmission cost, and not resilient to a large number of node compromises. To address the problem of data storage, transmission cost, and resilience of node compromise in UWSNs, we propose a novel Homomorphic Encryption and Homomorphic Secret Sharing based scheme (HEHSS) to accomplish the goals of confidentiality, resilience to node compromises, reliability, and efficiency of storage and transmission. Our scheme exploits the homomorphic secret sharing scheme to enable sensor nodes encode sensed data into several shares, and distribute them to their neighbors. This property allows the mobile sink to reconstruct data even if some of the shares stored in neighbors of a sensor are compromised. Detailed analysis has been conducted to evaluate the scheme in terms of efficiency and reliability.

A highly efficient algorithm towards optimal data storage and regeneration cost in multiple clouds

News is new information or information about something that is currently happening, presented in the form of print media, broadcasts, social media or word of mouth to third parties or multiple people. The spread of this news is now very much happening both from social media or messages. However, the problem is that no one can guarantee the truth of the news received. Text mining is the application of data mining concepts and techniques to look for patterns in text, the process of analyzing text to find useful information for a particular purpose. Another definition related to text mining is that text mining is data mining in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. To make it easier to identify news hoax requires a text mining algorithm, using a text mining algorithm can be useful to get real news. Based on the calculations of the TF-IDF algorithm, it can be concluded that the similarity of the meaning of the news whose truth is not yet known is closest to the news ofmerahputih.com with a confidence level of 23.11%.

Background: While it is commonly recognized that almost every work and research discipline utilize their own taxonomy, the language used within a specific discipline may also vary depending on numerous factors, including the desired effect of the information being communicated and the intended audience. Different audiences are reached through publication of information, including research results, in different types of publication outlets such as newspapers, newsletters, magazines, websites, and journals. Prior research has shown that students, both undergraduate and graduate, as well as faculty may have a difficult time locating information in different publication outlet types (e.g., magazines, newspapers, journals). The type of publication may affect the ease of understanding and also the confidence placed in the acquired information. A text analytics tool for classifying the source of research as a newsletter (used as a substitute for newspaper articles), a magazine, or an academic journal article has been developed to assist students, faculty, and researchers in identifying the likely source type of information and classifying their own writings with respect to these possible publication outlet types. Literature Review: Literature on information literacy is discussed as this forms the motivation for the reported research. Additionally, prior research on using text mining and text analytics is examined to better understand the methodology employed, including a review of the original Scale of Theoretical and Applied Research system, adapted for the current research. Research Questions: The primary research question is: Can a text mining and text analytics approach accurately determine the most probable publication source type with respect to being from a newsletter, magazine, or journal? Methodology: A text mining and text analytics algorithm, STAR’ (System for Text Analytics-based Ranking), was developed from a previously researched text mining tool, STAR (Scale of Theoretical and Applied Research), that was used to classify the research type of articles between theoretical and applied research. The new text mining method, STAR’, analyzes the language used in manuscripts to determine the type of publication. This method first mines all words from corresponding publication source types to determine a keyword corpus. The corpus is then used in a text analytics process to classify full newsletters, magazine articles, and journal articles with respect to their publication source. All newsletters, magazine articles, and journal articles are from the library and information sciences (LIS) domain. Results: The STAR’ text analytics method was evaluated as a proof of concept on a specific LIS organizational newsletter, as well as articles from a single LIS magazine and a single LIS journal. STAR’ was able to classify the newsletters, magazine articles, and journal articles with 100% accuracy. Random samples from another similar LIS newsletter and a different LIS journal were also evaluated to examine the robustness of the STAR’ method in the initial proof of concept. Following the positive results of the proof of concept, additional journal, magazine, and newsletter articles were used to evaluate the generalizability of STAR’. The second-round results were very positive for differentiating journals and newsletters from other publication types, but revealed potential issues for distinguishing magazine articles from other types of publications. Discussion: STAR’ demonstrates that the language used for transferring information within a specific discipline does differ significantly depending on the intended recipients of the research knowledge. Further work is needed to examine language usage specific to magazine articles. Conclusions: The STAR’ method may be used by students and faculty to identify the likely source of research or discipline-specific information. This may improve trust in the reliability of information due to different levels of rigor applied to different types of publications. Additionally, the STAR’ classifications may be used by students, faculty, or researchers to determine the most appropriate type of outlet and correspondingly the most appropriate type of audience for the reported information in their own manuscripts, thereby improving the chance for successful sharing of information to appropriate audiences who will deem the information to be reliable, through publication in the most relevant outlet type.

Bioinspired design is the adaptation of methods, strategies, or principles found in nature to solve engineering problems. One formalized approach to bioinspired solution seeking is the abstraction of the engineering problem into a functional need and then seeking solutions to this function using a keyword type search method on text based biological knowledge. These function keyword search approaches have shown potential for success, but as with many text based search methods, they produce a large number of results, many of little relevance to the problem in question. In this paper, we develop a method to train a computer to identify text passages more likely to suggest a solution to a human designer. The work presented examines the possibility of filtering biological keyword search results by using text mining algorithms to automatically identify which results are likely to be useful to a designer. The text mining algorithms are trained on a pair of surveys administered to human subjects to empirically identify a large number of sentences that are, or are not, helpful for idea generation. We develop and evaluate three text classification algorithms, namely, a Naïve Bayes (NB) classifier, a k nearest neighbors (kNN) classifier, and a support vector machine (SVM) classifier. Of these methods, the NB classifier generally had the best performance. Based on the analysis of 60 word stems, a NB classifier's precision is 0.87, recall is 0.52, and F score is 0.65. We find that word stem features that describe a physical action or process are correlated with helpful sentences. Similarly, we find biological jargon feature words are correlated with unhelpful sentences.

Semantic text classification: A survey of past and recent advances

The energy costs of transferring data from sources in Wireless Sensor Networks (WSNs) to sinks at the edge of the network are high. These high costs have generated much interest in data storage strategies within the WSN. The frequently dangerous and power limited conditions in which nodes are required to operate can however result in the loss of nodes that are vital data storage points. We extend work on a reliability enhancing WSN data storage strategy called Address Coding that provides power efficient self organizing backup capability in networks of homogeneous nodes. We show that the asymptotic energy cost per backup is lower with Address Coding than it would be for a naive storage strategy based on AODV. We also show that probabilistic flooding can be applied to Address Coding, yielding significant reduction in energy cost with negligible loss in reliability.

A Comparative Study on Various Text Mining Algorithms in Data Mining

This paper describes applications of the optimized pattern discovery framework to text and Web mining. In particular, we introduce a class of simple combinatorial patterns over phrases, called proximity phrase association patterns, and consider the problem of finding the patterns that optimize a given statistical measure within the whole class of patterns in a large collection of unstructured texts. For this class of patterns, we develop fast and robust text mining algorithms based on techniques in computational geometry and string matching. Finally, we successfully apply the developed text mining algorithms to the experiments on interactive document browsing in a large text database and keyword discovery from Web bases.

In text mining field, The KNN (K Nearest Neighbors) is one of the oldest and simplest methods of text classification. But it is known to be sensitive to the distance (or similarity) function used in classifying a test instance, this disadvantage can cause low classification accuracy and limit the KNN classifier's utilization in text classification in text mining. In this paper, we introduce Mahalanobis distance in text classification area, and proposed an algorithm (MDKNN) base on this theory. Experiment show that our method has comparable or better performance than KNN Classifier and Naïve Bayes classifier in text classification.

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

The 2nd International Symposium on Languages in Biology and Medicine (LBM2007) was held in Singapore in December 2007. It provided a renewed opportunity for interaction between language professionals with different methodological backgrounds. In particular original research and applications of language technologies in biology and medicine were solicited. The relevant themes are listed below. · Natural language: text mining, retrieval and management; · Ontology language: ontology construction, extension and management; · Logic language: knowledge representation and induction; · Sequence language: RNA structure prediction, protein domain prediction; · Database language: database interface, query language; · Visualization language: information visualization, molecular visualization; A total of 47 submissions were received and reviewed by a 37 strong program committee and 19 additional reviewers from Asia, Europe and North America. The committee represented the six afore mentioned research disciplines and participated in a double blind review process with 3 reviews per paper. A selection of 12 papers was accepted (25.5% acceptance rate) for long oral presentations during the symposium and publication in the LBM special issue of BMC Bioinformatics. A further 11 out of the remaining 35 papers were selected for short oral presentation (31.4% acceptance rate). This LBM special issue of BMC Bioinformatics consists of 10 long oral presentation papers, as some papers were withdrawn due to unforeseen circumstances. A proceedings of LBM short paper presentations comprising of 7 papers was published by CEUR and is available at http://ceur-ws.org/Vol-319. From these two editions 8 papers are from Asia (China 1, Japan 3, Korea 1, Singapore 1, TaiWan 2), and 9 papers are from Europe and North America (Finland 1, Hungary 1, Sweden 1, UK 4, Canada 1, USA 1). These papers were presented in five sessions, namely (a) Terminology and Named Entities, (b) Text Classification 1, (c) Text Classification 2, (d) Text Mining, (e) Ontology and Logic. In addition to technical paper presentations, the 3 keynote presentations of the symposium addressed terminology integration (Olivier Bodenreider), text mining services (Sophia Ananiadou) and ontology alignment (Patrick Lambrix). The panel discussion chaired by Junichi Tsujii concluded the symposium with an examination of the synergies among the biomedical language and knowledge technologies. In conclusion we express our deep appreciation to the program committee members and the additional reviewers who worked on a very tight schedule, sharing their valuable time and formidable expertise in support of the LBM review process. We also thank, Ho-Joon Lee from KAIST and Chen Bin from I2R / NUS for their assistance with the EasyChair system, the LBM website and other miscellaneous tasks. We also wish to thank Jong C. Park, Limsoon Wong, the two general chairs and See Kiong Ng for their help and suggestions.