Efficiency of cache-replacement algorithms while retrieving data from a relational database and XML files in a web based system

Caching has been applied in Web based information systems in order toreduce the transmission of redundant network traffic and response latency by savingcopies of the content obtained from the Web closer to the end user. The efficiencyof caching is influenced to a significant extent by the cache replacement algorithmswhich are triggered when the cache becomes full and old objects must be evicted tomake space for the new ones.This paper presents a framework that can be used in future work to tunecache-replacement algorithms while data is simultaneously retrieved from arelational database and XML files in a web based environment, by a large numberof end-users. Three replacement policies are considered: Least Recently Used(LRU), Least Frequently Used (LFU) and Lowest Latency First (LLF). Theexperimental results obtained from the framework show that data caching greatlyimproves the overall performance of web based systems, and the type of the appliedcache replacement policy also plays an important role in the performance. In thescenarios considered in this paper, the LLF algorithm produced the bestperformance when retrieving data from a relational database, while the LFUalgorithm was the most efficient algorithm when data was retrieved from an XMLfile

IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data

Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc

Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy

Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy

Provenance and social network analysis for recommender systems: a literature review

Recommender systems (RS) and their scientific approach have become very important because they help scientists find suitable publications and approaches, customers find adequate items, tourists find their preferred points of interest, and many more recommendations on domains. This work will present a literature review of approaches and the influence that social network analysis (SNA) and data provenance has on RS. The aim is to analyze differences and similarities using several dimensions, public datasets for assessing their impacts and limitations, evaluations of methods and metrics along with their challenges by identifying the most efficient approaches, the most appropriate assessment data sets, and the most appropriate assessment methods and metrics. Hence, by correlating these three fields, the system will be able to improve the recommendation of certain items, by being able to choose the recommendations that are made from the most trusted nodes/resources within a social network. We have found that content-based filtering techniques, combined with term frequency-inverse document frequency (TF-IDF) features are the most feasible approaches when combined with provenance since our focus is to recommend the most trusted items, where trust, distrust, and ignorance are calculated as weight in terms of the relationship between nodes on a network

Enrichment of Association Rules through Exploitation of Ontology Properties - Healthcare Case Study

Abstract Association rule mining as descriptive data mining category aims to find interesting patterns on data. The quality of the patterns is measured with two metrics: confidence and support. Especially in fields dealing with sensitive data, such as healthcare, the resulting patterns should be novel and interesting. To achieve that, not only the quality of the data itself should be superior, but also other additional attributes added, do support the results. That should be achieved by using Semantic Web technologies and thus enriching data used with semantic relations between properties. A hypothesis suggests that especially tackling property relations, chain property being part of the current version of the W3C Web Ontology Language (OWL), will yield better rules. To validate the hypothesis, experiments were performed on raw data, then on an older version of OWL, which does not support the chain properties and finally on the current version of language involving chain properties. Results obtained suggest that the latter produces novel rules with strong confidence and support, not encountered in former two experiments

Ontology-based access to heterogeneous XML data

Abstract: With the increase in popularity of XML on the Internet, the requirements of database management systems have shifted from traditional transaction-based databases towards the kind of characteristics provided, by design, by the Lightweight Directory Access Protocol. At the same time, the design and use of a middleware to provide a common querying interface to XML-based systems has become an increasingly relevant research problem, encouraged by the fact that XML has become the de facto standard for information interchange on the Internet. The purpose of this paper is to describe the capabilities of our LDAP-based middleware that is able to transparently incorporate arbitrary XML documents, address structural discrepancies among XML data sources, and provide support for the resolution of semantic mismatches under a common framework, thanks to the simplicity, coherence and uniformity of the LDAP model

Human-annotated dataset for social media sentiment analysis for Albanian language

Social media was a heavily used platform by people in different countries to express their opinions about different crises, especially during the Covid-19 pandemics. This dataset is created through collecting people’s comments in the news items on the official Facebook site of the National Institute of Public Health of Kosovo. The dataset contains a total of 10,132 comments that are human-annotated in the Albanian language as a low-resource language. The dataset was collected from March 12, 2020, and this coincides with the emergence of the first confirmed Covid-19 case in Kosovo until August 31, 2020, when the second wave started. Due to the scarcity of labeled data for low-resource languages, the dataset can be used by the research community in the field of machine learning, information retrieval, affective computing, as well as by the public agencies and decision makers