Leonidas Deligiannidis, Amit Sheth, Boanerges Aleman-Meza · Scholar Commons (University of South Carolina) · 2006
Abstract. In this paper we present a new tool for semantic analytics through 3D visualization called “Semantic Analytics Visualization ” (SAV). It has the capability for visualizing ontologies and meta-data including annotated webdocuments, images, and digital media such as audio and video clips in a synthetic three-dimensional semi-immersive environment. More importantly, SAV supports visual semantic analytics, whereby an analyst can interactively investigate complex relationships between heterogeneous information. The tool is built using Virtual Reality technology which makes SAV a highly interactive system. The backend of SAV consists of a Semantic Analytics system that supports query processing and semantic association discovery. Using a virtual laser pointer, the user can select nodes in the scene and either play digital media, display images, or load annotated web documents. SAV can also display the ranking of web documents as well as the ranking of paths (sequences of links). SAV supports dynamic specification of sub-queries of a given graph and displays the results based on ranking information, which enables the users to find, analyze and comprehend the information presented quickly and accurately. 1
Cited by 3Open access
Boanerges Aleman-Meza, Amit Sheth, Devanand Palaniswami et al. · IGI Global eBooks · 2011
We describe an ontological approach for determining the relevance of documents based on the underlying concept of exploiting complex semantic relationships among real-world entities. This research builds upon semantic metadata extraction and annotation, practical domain-specific ontology creation, main-memory query processing, and the notion of semantic association. A prototype application illustrates the approach by supporting the identification of insider threats for document access. In this scenario, we describe how investigative assignments performed by intelligence analysts are captured into a context of investigation by including concepts andrelationships from the ontology. A relevance measure for documents is computed using semantic analytics techniques. Additionally, a graph-based visualization component allows exploration of potential document access beyond the ‘need to know’. We also discuss how a commercial product using Semantic Web technology, Semagix Freedom, is used for metadata extraction when designing and populating an ontology from heterogeneous sources.Request access from your librarian to read this chapter's full text.
Cited by 0
Perry, Matthew, Sheth, Amit P., Arpinar, Ismailcem Budak et al. · Scholar Commons (University of South Carolina) · 2009
The amount of digital data available to researchers and knowledge workers has grown tremendously in recent years. This is especially true in the geography domain. As the amount of data grows, problems of data relevance and information overload become more severe. The use of semantics has been proposed to combat these problems (Berners-Lee et al., 2001; Egenhofer, 2002). Semantics refer to the meaning of data rather than its syntax or structure. Systems which can understand and process data at a semantic level can achieve a higher level of automation, integration, and interoperability. Applications generally use semantic technology for three basic purposes: (1) semantic integration, (2) semantic search and contextual browsing, and (3) semantic analytics and knowledge discovery (Sheth & Ramakrishnan, 2003).
Cited by 0
Le, Van-Hoang · Figshare · 2024
Large-scale software-intensive systems often produce a large volume of logs to record runtime status and events for troubleshooting purposes. Logs play an important role in the maintenance and operation of software systems, which allow engineers to better understand the system's behaviours and diagnose problems. The rich information included in log data enables a variety of software reliability management tasks, such as anomaly detection, root cause analysis, and failure prediction. As the scale and complexity of software systems increase, traditional log analytics approaches becomes time-consuming and error-prone due to the rapid growth of log data volume and the complexity of log data semantics. In this thesis, we propose intelligent approaches for semantic-aware log analytics to effectively utilize log data in software reliability management. Firstly, we conduct an empirical study on log-based anomaly detection with deep learning. Log-based anomaly detection plays a vital role in software reliability management. Recently, many deep learning models have been proposed to automatically detect system anomalies based on log data and achieve high detection accuracy. To achieve a profound understanding of how far we are from solving the problem of log-based anomaly detection, we conduct an in-depth analysis of five state-of-the-art deep learning-based models for detecting system anomalies. We obtain five insightful findings and make these methods open-source for easy reuse and further study. Secondly, we propose to a novel deep learning-based approach that detect system anomalies from raw log messages. Existing anomaly detection approaches require to convert raw log messages into structured data, which might be error-prone due to the semantic misunderstanding problem from log data. To tackle this challenge, we propose NeuralLog to extract the semantic meaning of raw log messages and represents them as semantic vectors. These representation vectors are then used to detect anomalies through a Transformer-based classification model, which can capture the contextual information from log sequences. Experimental results on four real-world datasets confirm the effectiveness of our proposed method. Thirdly, we propose a semantic-aware log parsing method powered by prompt-based few-shot learning. Log parsing, which extract log templates associated with dynamic parameters, is considered as the first step of many log-based reliability management methods. Existing log parsing methods extract the common part as log templates using statistical features and often fail to identify the correct templates and parameters because they often overlook the semantic meaning of log messages and require domain-specific knowledge for different log datasets. To address the limitations of existing methods, we propose LogPPT to capture the semantic information of log messages to identify log events and parameters based on a few labelled log data. Experimental results on 16 real-world datasets show that LogPPT is effective and efficient for log parsing. Fourthly, we propose to pre-train a language model with semantic awareness using heterogeneous log data to unify many log analytics tasks into a single framework through. Existing approaches for intelligent log analytics are specifically designed for a certain type of tasks and cannot generalise to other tasks. Therefore, we propose PreLog, a pre-trained model with contrastive learning. PreLog is pre-trained on a large amount of log data with two log-specific objective and is generalised to downstream tasks. Extensive experimental results show that PreLog achieves better or comparable results in comparison with state-of-the-art, task-specific methods. Finally, we explore the application of ChatGPT, the current cutting-edge large language model (LLM), to perform log parsing without model training. Experimental results show that ChatGPT can achieve good results for log parsing with appropriate prompts, especially with few-shot prompting. Our findings indicate that applying LLMs to log analytics is a promising direction. We outline several challenges and opportunities for LLMs-based log analytics as well as discuss the potential future works. In summary, this thesis targets the design of semantic-aware approaches toward intelligent log analytics. Comprehensive experiments on public datasets demonstrate the effectiveness of our proposed methods.
Cited by 0Open access
Matthew Perry, Amit Sheth, I. Budak Arpinar et al. · IGI Global eBooks · 2011
The amount of digital data available to researchers and knowledge workers has grown tremendously in recent years. This is especially true in the geography domain. As the amount of data grows, problems of data relevance and information overload become more severe. The use of semantics has been proposed to combat these problems (Berners-Lee et al., 2001; Egenhofer, 2002). Semantics refer to the meaning of data rather than its syntax or structure. Systems which can understand and process data at a semantic level can achieve a higher level of automation, integration, and interoperability. Applications generally use semantic technology for three basic purposes: (1) semantic integration, (2) semantic search and contextual browsing, and (3) semantic analytics and knowledge discovery (Sheth & Ramakrishnan, 2003). Request access from your librarian to read this chapter's full text.
Cited by 2
Dominik Ślȩzak, Andrzej Janusz, Wojciech Świeboda et al. · QUT ePrints (Queensland University of Technology) · 2011
Cited by 0
Martelli, Federico, Maru, Marco, Campagnano, Cesare et al. · Universidade Nova de Lisboa's Repository (Universidade Nova de Lisboa) · 2022
UIDB/03213/2020 UIDP/03213/2020
Cited by 0Open access
Bojan Cestnik, Stojan Košti · 2010
Business analytics (BA) involves defining organization's capabilities and requirements in order to understand how it functions to accomplish its purposes. BA is typically performed to define and validate solutions that are designed to meet organization's business needs, goals or objectives. To pursue this mission, analysts have to collect, analyse and synthesise huge amount of information. In this paper we demonstrate how constructing semantic ontologies can increase the level of automation of BA methods and techniques. This can be achieved by sophisticated semantic data analysis that can help us structure information and obtain birds-eye overview of a given domain. For the case study we take a set of abstracts from the articles presented on CompSysTech'09 conference and use OntoGen tool to semi-automatically construct ontology for the domain. The obtained results indicate that the proposed approach can be effectively used to digest textual information and present it in a more operational form of topic ontology. We argue that such approach can be useful also to generate domain knowledge in the field of BA.
Cited by 0
R Aravindhan, R Shanmugalakshmi, H Hui et al. · International Journal of Latest Trends in Engineering and Technology · 2016
Semantic-based image retrieval (SBIR) is a retrieval technique based on the intuition of user. However, implementing a meticulous strategy without incurring a semantic gap (disparity between the retrieved images and the user desired images) is a challenging task. The semantic gap is not trivial, when it comes to sensitive medical diagnostic images. Also, with rapid digitization of medical diagnostic images, statistics reveal that data from US health care system alone reached 150 Exabyte's and so with the presumption that this diagnostic medical data will shoot up, memory requirements and computational expenses should be reduced substantially. Furthermore, as only a region of image will be most relevant (ROI-Region of Interest) while retrieving diagnostic medical data, the feature dimensionality is reduced and thereby the speed of the retrieval process is increased. As, most SBIR algorithms do not intuitively explain why the retrieved images are considered relevant to the user query, there is a chance that the user may miss out relevant images which may be outliers. Semantic technologies like ontology offers auspicious approach to image retrieval as it tries to map the low level image features to high level ontology concepts. So a tool called Visual Analytics Tool for Semantic Retrieval (VATSR) is proposed which allows user to visually interact and refine the query and/or search results. Moreover, in this paper, a hierarchical classifier is proposed which classifies at different levels with different set of features, which ensures that a relevant image missed out in one classifier may be retrieved in another classifier. Along with this, a boosting algorithm is use to improve the precision of the search results.
Cited by 4Open access
Mark Daniel Rintoul · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2022
Abstract not provided.
Cited by 0Open access
Slupczynski, Michal Piotr, Decker, Stefan Josef · RWTH Publications (RWTH Aachen) · 2023
Cited by 0Open access
Madhushi Bandara, Fethi Rabhi · Semantic Web · 2019
Data Analytics Solution (DAS) engineering often involves multiple tasks from data exploration to result presentation which are applied in various contexts and on different datasets. Semantic modeling based on the open world assumption supports flexible modeling of linked knowledge. The objective of this paper is to review existing techniques that leverage semantic web technologies to tackle challenges such as heterogeneity and changing requirements in DAS engineering. We explore the application scope of those techniques, the different types of semantic concepts they use and the role these concepts play during DAS development process. To gather evidence for the study we performed a systematic mapping study by identifying and reviewing 82 papers that incorporate semantic models in engineering DASs. One of the paper’s findings is that existing models can be classified within four types of knowledge spheres: domain knowledge, analytics knowledge, services and user intentions. Another finding is to show how this knowledge is used in literature to enhance different tasks within the analytics process. We conclude our study by discussing the limitations of the existing body of research, showcasing the potential of semantic modeling to enhance DASs and presenting the possibility of leveraging ontologies for effective end-to-end DAS engineering.
Cited by 13Open access
Sheth, Amit P. · Journal of Bioresource Management · 2005
Semantics is seen as the key ingredient in the next phase of the Web infrastructure as well as the next generation of enterprise content management. Ontology is the centerpiece of the most prevalent semantic technologies and provides the basis of representing, acquiring, and utilizing knowledge. With the availability of several commercial products and many research tools, specifications and increasing adoption of Semantic Web standards such as RDF for metadata and OWL for ontology representation, ontology-driven techniques and systems have already enabled a new generation of industry strength semantic applications. In particular, Semagix's Freedom has powered applications in leading verticals such as, financial services, government & intelligence, pharmaceuticals, and media & entertainment. In this paper, we portray some of the requirements of high-end enterprise applications requiring search to integration, and more advanced analytical capabilities, discuss the enterprise scale capabilities expected of a semantic technology, and how Semagix has put an ontology-driven approach to use.
Cited by 1Open access
I‐Han Hsiao, Sesha Kumar Pandhalkudi Govindarajan, Yiling Lin · 2016
We designed and studied an innovative semantic visual learning analytics for orchestrating today's programming classes. The visual analytics integrates sources of learning activities by their content semantics. It automatically processs paper-based exams by associating sets of concepts to the exam questions. Results indicated the automatic concept extraction from exams were promising and could be a potential technological solution to address a real world issue. We also discovered that indexing effectiveness was especially prevalent for complex content by covering more comprehensive semantics. Subjective evaluation revealed that the dynamic concept indexing provided teachers with immediate feedback on producing more balanced exams.
Cited by 18
A. Bharathi Malakreddy, S. Venkataraman, Mohammed Sinan Khan et al. · Lecture notes in networks and systems · 2023
Cited by 2
Gloria Fernández, Olga Mariño · Lecture notes in educational technology · 2015
Cited by 3
Andrea Giordano · QRU Quaderns de Recerca en Urbanisme · 2017
One of the most interesting research fields in IT industries is the quest of software able to deal efficiently with Big Data. In this thesis, we have studied Big Data especially about their spread in healthcare, where data are generated at a high rate and the use of computer science can save lives.
Cited by 1Open access
Draschner, Carsten Felix, Moghaddam, Farshad Bakhshandegan, Lehmann, Jens et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024
Abstract. Linked open data sources and the semantic web has become a precious data source for data analytics tasks and data integration. The growing data set sizes of RDF Knowledge Graph data need scalable processingand analytics techniques. The processing power of in-memory frameworks which can perform scalable distributed semantic analytics like SANSA, make use of Apache Spark and Apache Jena to providestart-to-end extensive scalable analytics on RDF knowledge graphs. The setup of a technical system with all dependencies and environments can be a tough challenge and might also require sufficient available processingpower. To reduce the entry barriers for getting started in evaluating and testing all opportunities of the SANSA framework and even bring this technology to production only from the browser. We introduce within this paper how to get the SANSA stack running within Databricks, with no need for special Apache Spark skills or any installations. This simplified usage offers distributed large-scale processing of RDF data from mobile devices. In addition, the availability of Hands-On Sample Notebooks increases the reproducibility of complex framework evaluation experiments. This paper shows that the startup of a very complex scalable semantic data analytics stack framework does not need to be complicated.
Cited by 0Open access
Amit Sheth · 2005
Abstract. Semantics is seen as the key ingredient in the next phase of the Web infrastructure as well as the next generation of enterprise content management. Ontology is the centerpiece of the most prevalent semantic technologies and provides the basis of representing, acquiring, and utilizing knowledge. With the availability of several commercial products and many research tools, specifications and increasing adoption of Semantic Web standards such as RDF for metadata and OWL for ontology representation, ontology-driven techniques and systems have already enabled a new generation of industry strength semantic applications. In particular, Semagix’s Freedom has powered applications in leading verticals such as, financial services, government & intelligence, pharmaceuticals, and media & entertainment. In this paper, we portray some of the requirements of high-end enterprise applications requiring search to integration, and more advanced analytical capabilities, discuss the enterprise scale capabilities expected of a semantic technology, and how Semagix has put an ontology-driven approach to use. 1
Cited by 0
Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das et al. · IEEE Transactions on Mobile Computing · 2024
The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. This paper focuses on how individuals’ GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named <i>Mobilytics</i> designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) and a deep learning architecture that automatically annotates the GPS log. Additionally, we propose a novel “transfer learning” approach to explore movement dynamics in a geographically distant area by leveraging knowledge obtained from a comparable region, such as an academic campus. In terms of major contributions and novelty, this is the first work to present end-to-end daily mobility trip purpose extraction and mobility knowledge transfer for trip annotation and POI-tagging where the labeled data are insufficient. Experimental results on real-life datasets of five different regions demonstrate the efficacy of our proposed Mobilytics framework which outperforms the baselines for trip-purpose extraction and POI annotations by a significant margin (<inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 18% to <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 30%). Moreover, the analysis on huge volume of simulated traces (10,000 users) illustrates the scalability and robustness of the framework.
Cited by 2