Metaverse Knowledge Graph Construction: An Unsupervised Relation Extraction Approach based on Semantic Mining

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Li, Litao; Xu, Ruiyun; Zhao, J. Leon

Abstract: Metaverse has gained widespread attention in both academia and industry. As a r ... Expand

Abstract: Metaverse has gained widespread attention in both academia and industry. As a result, many new concepts, methodologies, and applications are emerging in recent years. Constructing a Metaverse knowledge graph can help with knowledge management and facilitate various intelligent tasks such as recommendations, information retrieval, and chatbots in this emerging field. However, existing knowledge graph construction approaches are not directly applicable in the Metaverse domain due to a lack of labeled dataset, making existing supervised relation extraction approaches impracticable. In this study, we develop a novel graph construction framework by integrating the deep learning approach (i.e., BERT-BiLSTM-CRF) with an unsupervised relation extraction approach based on semantic mining. The framework we propose in this paper can automatically construct knowledge graphs while significantly reducing labeling efforts. Further, we plan to implement the proposed framework on a Metaverse corpus to construct a Metaverse knowledge graph. To test our approach, we will employ an expert-survey method to evaluate the quality of triple sets. Collapse

Topics: virtual reality knowledge graph mobile application blog artificial intelligence

Methods: BERT survey word2vec deep learning named entity recognition

The Design of Open Platforms: Towards an Emulation Theory

2023 | HICSS | Citations: 0

Authors: Rudmark, Daniel; Lindgren, Rikard

Abstract: The enrolment of third-party developers is essential to leverage the creation an ... Expand

Abstract: The enrolment of third-party developers is essential to leverage the creation and evolution of data ecosystems. When such complementary development takes place without any organizational consent, however, it causes new social and technical problems to be solved. In this paper, we advance platform emulation as a theoretical perspective to explore the nature of such problem-solving in the realm of open platforms. Empirically, our analysis builds on a 10-year action design research effort together with a Swedish authority. Its deliberate change agenda was to transform unsolicited third-party development into a sanctioned data ecosystem, which led to a live open platform that is still in production use. Theoretically, we synthesize and extend received theory on open platforms and offer novel product and process principles for this class of digital platforms. Collapse

Topics: digital platform application programming interface mobile application problem solving database languages

Methods: qualitative interview action design research design science data modeling triangulation

SENS: Semantic Synthetic Benchmarking Model for Integrated Supply Chain Simulation and Analysis

2022 | European Conference On Information Systems | Citations: 0

Authors: Ramzy, Nour; Auer, Sören; Ehm, Hans; Chamanara, Javad

Abstract: Supply Chain (SC) modeling is essential to understand and influence SC behavior ... Expand

Abstract: Supply Chain (SC) modeling is essential to understand and influence SC behavior, especially for increasingly globalized and complex SCs. Existing models address various SC notions, e.g., processes, tiers and production, in an isolated manner limiting enriched analysis granted by integrated information systems. Moreover, the scarcity of real-world data prevents the benchmarking of the overall SC performance in different circumstances, especially wrt. resilience during disruption. We present SENS, an ontology-based Knowlegde-Graph (KG) equipped with SPARQL implementations of KPIs to incorporate an end-toend perspective of the SC including standardized SCOR processes and metrics. Further, we propose SENS-GEN, a highly configurable data generator that leverages SENS to create synthetic semantic SC data under multiple scenario configurations for comprehensive analysis and benchmarking applications. The evaluation shows that the significantly improved simulation and analysis capabilities, enabled by SENS, facilitate grasping, controlling and ultimately enhancing SC behavior and increasing resilience in disruptive scenarios. Collapse

Topics: knowledge representation database languages supply chain management

Methods: computational algorithm simulation ontological modelling literature study

A Query Language for Workflow Logs

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Tang, Yan; Cui, Weilong; Su, Jianwen

Abstract: A business process (workflow) is an assembly of tasks to accomplish a business g ... Expand

Abstract: A business process (workflow) is an assembly of tasks to accomplish a business goal. Real-world workflow models often demanded to change due to new laws and policies, changes in the environment, and so on. To understand the inner workings of a business process to facilitate changes, workflow logs have the potential to enable inspecting, monitoring, diagnosing, analyzing, and improving the design of a complex workflow. Querying workflow logs, however, is still mostly an ad hoc practice by workflow managers. In this article, we focus on the problem of querying workflow log concerning both control flow and dataflow properties. We develop a query language based on “incident patterns” to allow the user to directly query workflow logs instead of having to transform such queries into database operations. We provide the formal semantics and a query evaluation algorithm of our language. By deriving an accurate cost model, we develop an optimization mechanism to accelerate query evaluation. Our experiment results demonstrate the effectiveness of the optimization and achieves up to 50× speedup over an adaption of existing evaluation method. Collapse

Topics: database languages structured query language database system business process management relational database

Methods: computational algorithm experiment optimization model business process model and notation modeling language

Peak cubes in service operations: Bringing multidimensionality into decision support systems

2021 | Decision Support Systems | Citations: 0

Authors: Barfar, Arash; Padmanabhan, Balaji; Hevner, Alan

Abstract: Companies like Ritz Carlton, Disney and Verizon are among many who have invested ... Expand

Abstract: Companies like Ritz Carlton, Disney and Verizon are among many who have invested in analytics to improve their customers’ service experiences with the firms. Extensive data are collected on all aspects of how customers interact or experience the products or services. Research has shown the importance of the “peak-end” rule in service design; that is, providing a customer with good “peak” service levels and “ending” the service experience with high quality can enhance customer satisfaction and build loyalty. However, previous studies have examined this phenomenon only in contexts with unidimensional service levels. We introduce peak cubes, which enable service designers and scholars to pinpoint prominent service levels in multidimensional service experience pro­ files—thereby extending current research on behavioral economics and service design to more general settings. Results indicate the potential of multidimensional peak-end models to better predict customer satisfaction in various service scenarios. Using Shapley values in coalitional game theory, the resulting models can also inform service designers about the quality dimensions that are critical from the perspective of multidimensional peakend heuristic and customer satisfaction. Our research contributions and proposed methodology will enhance decision support systems with multidimensional capabilities and have applications to fields as diverse as service operations and healthcare. Collapse

Topics: service quality shapley additive explanation logistics management customer satisfaction database system

Methods: simulation computational algorithm time series analysis longitudinal research LightGBM

Theories: behavioral economics theory of bounded rationality game theory cooperative game theory

TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

2021 | Information Systems Frontiers | Citations: 0

Authors: Ghrab, Amine; Romero, Oscar; Skhiri, Sabri; Zimányi, Esteban

Abstract: Graphs are a fundamental structure that provides an intuitive abstraction for mo ... Expand

Abstract: Graphs are a fundamental structure that provides an intuitive abstraction for modeling and analyzing complex and highly interconnected data. Given the potential complexity of such data, some approaches proposed extending decision-support systems with multidimensional analysis capabilities over graphs. In this paper, we introduce TopoGraph, an end-to-end framwork for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints. This results in defining three new types of graph cubes: property graph cubes, topological graph cubes, and graph-structured cubes. Afterwards, we define the algebraic OLAP operations for such novel cubes. We implement and experimentally validate TopoGraph with different types of real-world datasets. Collapse

Topics: online analytical processing Twitter Neo4j application programming interface social network

Methods: computational algorithm multidimensional modeling experiment design artifact cluster analysis

An appraisal mechanism for a social marketplace

2021 | Information & Management | Citations: 0

Authors: Li, Yung-Ming; Lin, Lien-Fa; Lien, Ming Yen

Abstract: Social networks have become indispensable in our daily lives, and have significa ... Expand

Abstract: Social networks have become indispensable in our daily lives, and have significant implications for the devel­ opment of electronic commerce. In centralized e-marketplaces, buyers face two main problems in purchasing decisions. Firstly, for some products, sellers do not provide product information. Secondly, buyers can be deceived by fake reviews or ratings manipulated by malicious providers. In this research, after analyzing users’ social activities, we propose an appraisal mechanism for the social marketplace, which customers can use to find products and to obtain credible referrals. The proposed mechanism can effectively discover suitable and trust­ worthy sellers to expedite the online purchasing process and enable social commerce to flourish. Collapse

Topics: social network Facebook electronic market electronic commerce mobile phone

Methods: survey parametric test paired t-test experiment statistical hypothesis test

Approaches to Enforce Privacy in Databases: Classical to Information Flow-Based Models

2021 | Information Systems Frontiers | Citations: 0

Authors: Shyamasundar, R.K.; Chaudhary, Pratiksha; Jaiswal, Arushi; Kuiri, Aniket

Abstract: Ever since databases became an ubiquitous part of enterprises or businesses, sec ... Expand

Abstract: Ever since databases became an ubiquitous part of enterprises or businesses, security and privacy became a requirement. Traditionally, privacy was realized through various methods of database access control and relied much on the use of statically defined views, which are essentially logical constructs imposed over database tables that can alter or restrict the data that can be viewed by an user. Privacy is about the responsible maintenance of private information. This responsibility is hard to define, which is why laws are necessary. With a vast accumulation of personal data in databases, there has been a heightened awareness and concern about the storage and use of private information leading to privacy-related guidelines, regulations and legislations, Compliance with these regulations has become one of the major concerns for organizations and companies. Traditionally, privacy in databases (DBs) have been addressed through access control techniques including multi-level security (MLS) based on mandatory access control (MAC), and restricted views to the users. As view definitions to comply with regulations became quite complex for accommodating all the restrictions in one view, explicit constructs for specifying privacy policies were introduced for complying with medical regulations like HIPAA (Health Insurance Portability and Accountability Act) from USA, in relational database systems. These enabled fine grained access control (FGAC) capable of enforcing disclosure control enunciated databases. Application of information flow control that is needed for multi-level security (MLS) databases to preserve privacy among multiple users but have their challenges like new abstractions for managing information flow in a relational database system, handling transactions and integrity constraints without introducing covert channels etc. As the DBs need to work alongside information flow controlled programming languages and operating systems for tracking flows, there is a need to enforce the security policy not only on the DBMS but also on the application platform. Due to the underlying requirement of decentralization, it calls for declassification/endorsement and santization requirements on the DB. In this paper, we shall first review some of the major privacy enhancing techniques used traditionally for DBs including MLS DBs, and then explore application of decentralized information flow control models for realizing information flow secure DBs in a robust manner. Towards the end, we shall also touch upon some of the roles of anonymization and psuedonymization including inference control and differential privacy in realizing privacy in practice. Collapse

Topics: privacy PostgreSQL database system differential privacy authorization

Methods: design science case study design artifact database modeling data modeling

TextBenDS: a Generic Textual Data Benchmark for Distributed Systems

2021 | Information Systems Frontiers | Citations: 0

Authors: Truică, Ciprian-Octavian; Apostol, Elena-Simona; Darmont, Jérôme; Assent, Ira

Abstract: Extracting top-k keywords and documents using weighting schemes are popular tech ... Expand

Abstract: Extracting top-k keywords and documents using weighting schemes are popular techniques employed in text mining and machine learning for different analysis and retrieval tasks. The weights are usually computed in the data preprocessing step, as they are costly to update and keep track of all the modifications performed on the dataset. Furthermore, calculation errors are introduced when analyzing only subsets of the dataset, i.e., wrong weighting are computed as weighting schemes use the number of documents for scoring keywords and documents. Therefore, in a Big Data context, it is crucial to lower the runtime of computing weighting schemes, without hindering the analysis process and the accuracy of the machine learning algorithms. To address this requirement for the task of computing top-k keywords and documents (which largely relies on weighting schemes), it is customary to design benchmarks that compare weighting schemes within various configurations of distributedframeworks and database management systems. Thus, we propose TextBenDS - a generic document-oriented benchmark for storing textual data and constructing weighting schemes. Our benchmark offers a generic data model designed with a multidimensional approach for storing text documents. We also propose using aggregation queries with various complexities and selectivities for constructing term weighting schemes, that are utilized in extracting top-k keywords and documents. We evaluate the computing performance of the queries on several distributed environments set within the Apache Hadoop ecosystem. Our experimental results provide interesting insights. As an example, MongoDB shows the best overall performance, while Spark’s execution time remains almost constant regardless of weighting schemes. Collapse

Topics: information retrieval MongoDB database system distributed system MapReduce

Methods: term frequency–inverse document frequency Okapi BM25 experiment design science qualitative content analysis

Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores

2020 | International Journal of Information Management | Citations: 0

Authors: Ravat, Franck; Song, Jiefu; Teste, Olivier; Trojahn, Cassia

Abstract: This paper proposes an approach to tackle the problem of querying large volume o ... Expand

Abstract: This paper proposes an approach to tackle the problem of querying large volume of statistical RDF data. Our approach relies on pre-aggregation strategies to better manage the analysis of this kind of data. Specifically, we define a conceptual model to represent original RDF data with aggregates in a multidimensional structure. A set of translations rules for converting a well-known multidimensional RDF modelling vocabulary into the proposed conceptual model is then proposed. We implement the conceptual model in six different data stores: two RDF triple stores (Jena TDB and Virtuoso), one graph-oriented NoSQL database (Neo4j), one column-oriented data store (Cassandra), and two relational databases (MySQL and PostGreSQL). We compare the querying performance, with and without aggregates, in these data stores. Experimental results, on real-world datasets containing 81.92 million triplets, show that pre-aggregation allows for reducing query runtime in all data stores. Neo4j NoSQL and relational databases with aggregates outperform triple stores speeding up to 99% query runtime. Collapse

Topics: Neo4j Cassandra relational database nosql database MySQL

Methods: experiment conceptual modelling computational algorithm multidimensional modeling optimization model