A RESTful and semantic framework for data integration

Companies have to deal with huge amounts of heterogeneous information, usually stored in distributed datasets that make use of different data schemas. This topic is especially crucial for enterprises that deal with new and different kinds of business data as new services are provided; they need to be able to dynamically add new datasets with new schemas to their information systems. However, even though research efforts have been applied to deal with this integration problem, there is still a lack of practical approaches ready to be implemented for industrial cases. We present a web‐based architecture and system built upon ontologies and other semantic web techniques to cope with federation of business data in real time. The scenario used to demonstrate the utility of the architecture is composed of actual data of a telecom company. Results show that our solution is more suitable, efficient and practical than other works. Copyright © 2014 John Wiley & Sons, Ltd.     

A foundation for capturing and querying complex multidimensional data

On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead.     

Comprehensive approach to semantic similarity for rapid data integration

It is greatly significant to combine data with diverse structures and supply a unified view of these data for the user, especially for sharing data residing in heterogeneous data sources via the internet. This paper introduces a fast and novel method to data integration among different systems, which is based on ontology similarity in a language agnostic way. The fundamental ontological entities are extracted from multiple data sources according to the same mapping rules. By means of the improved edit distance algorithm, similarity measurement consists of determining the levels of similarity among the ontological entities for aiding in the construction of data integration platform. A web-service based architecture is presented along with the set of layers designed to achieve rapid data integration from different aspects such as data center, ontology extraction and similarity measurement, which aims to make this architecture more flexible. The prototype implemented by the proposed approach shows satisfying results against other techniques.     

G-tree: a new data structure for organizing multidimensional data

The author describes an efficient data structure called the G-tree (or grid tree) for organizing multidimensional data. The data structure combines the features of grids and B-trees in a novel manner. It also exploits an ordering property that numbers the partitions in such a way that partitions that are spatially close to one another in a multidimensional space are also close in terms of their partition numbers. This structure adapts well to dynamic data spaces with a high frequency of insertions and deletions, and to nonuniform distributions of data. We demonstrate that it is possible to perform insertion, retrieval, and deletion operations, and to run various range queries efficiently using this structure. A comparison with the BD tree, zkdb tree and the KDB tree is carried out, and the advantages of the G-tree over the other structures are discussed. The simulated bucket utilization rates for the G-tree are also reported     

On-demand e-supply chain integration: A multi-agent constraint-based approach

With e-business emerging as a key enabler to drive supply chains, the focus of supply chain management has been shifted from production efficiency to customer-driven and partnership synchronization approaches. This strategic shift depends on the match between the demands and offerings that deliver the services. To achieve this, we need to coordinate the flow of information among the services, and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The real-world situation is complicated as a result of undetermined requirements of services involved in the chain, unpredictable solutions contributed by service providers, and dynamic selection and aggregation of solutions to services. This paper examines an agent-mediated approach to on-demand e-business supply chain integration. Each agent works as a service broker, exploring individual service decisions as well as interacting with each other for achieving compatibility and coherence among the decisions of all services. Based on the framework, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

大规模差异化点云数据下的联邦语义分割算法

海量点云数据的存储对自动驾驶实时3D协同感知具有重要意义,然而出于数据安全保密性的要求,部分数据拥有者不愿共享其私人的点云数据,限制了模型训练准确性的提升。联邦学习是一种注重数据隐私安全的计算范式,提出了一种基于联邦学习的方法来解决车辆协同感知场景下的大规模点云语义分割问题。融合具有点间角度信息的位置编码方式并对邻近点进行几何衍射处理以增强模型的特征提取能力,最后根据本地模型的生成质量动态调整全局模型的聚合权重,提高数据局部几何结构的保持能力。在SemanticKITTI,SemanticPOSS和Toronto3D三个数据集上进行了实验,结果表明该算法显著优于单一训练数据和基于FedAvg的方法,在充分挖掘点云数据价值的同时兼顾各方数据的隐私敏感性。     

语义网格环境中异构数据资源整合研究

目前,各大高校信息资源采用不同的实现平台、不同的数据结构和实现方式,并且数据库在地理上是分布的更是异构的,给用户选择利用高校信息资源带来很大的不便,因此,如何消除各高校之间的"信息孤岛"现象,提供一个有效的机制,已成为当前信息化进程中急需解决的问题,运用语义网格理论,构造语义网格模型CI-Grid,可以实现一种利用本体整合分布式异构数据库资源的机制,能有效地解决这一问题,并有较高的应用前景.     

Learning from data streams for automation and orchestration of 6G industrial IoT: toward a semantic communication framework

Neural Computing and Applications - Established methods of communication are based mainly on Shannon’s theory of information, which purposefully overlooks semantic elements of communication....     

Gratis lattice points for multidimensional integration

The computation of optimal coefficients for higher dimensionss and larger modulesN by means of the methods known hitherto leads to practically insurmountable problems regarding the computing time needed. In this note we give a method for computing “useful coefficients”, where the computation of these coefficients is immediate and where the computing time is practically negligible for anys andN. Whereas the theoretical efficiency of those “useful coefficients” is roughly speaking half the efficiency of the best possible coefficients, all practical tests indicate that our methods lead to optimal performance as well. A series of computational comparisons between the “useful coefficients” and the optimal ones is enclosed.     

Designing a visualization framework for multidimensional data

Interest in visualization has grown in recent years, producing rapid advances in the diversity of research and in the scope of proposed techniques. Much of the initial focus in computer-based visualization concentrated on display algorithms, often for specific domains. For example, volume, flow, and terrain visualization techniques have generated significant insights into fundamental graphics and visualization theory, aiding the application experts who use these techniques to advance their own research. More recent work has extended visualization to abstract data sets like network intrusion detection, recommender systems, and database query results. This article describes our initial end-to-end system that starts with data management and continues through assisted visualization design, display, navigation, and user interaction. The purposes of this discussion are to (i) promote a more comprehensive visualization framework; (ii) describe how to apply expertise from human psychophysics, databases, rational logic, and artificial intelligence to visualization; and (iii) illustrate the benefits of a more complete framework using examples from our own experiences.     

Automated discovery and integration of semantic urban data streams: The ACEIS middleware

With the growing popularity of Internet of Things (IoT) technologies and sensors deployment, more and more cities are leaning towards smart cities solutions that can leverage this rich source of streaming data to gather knowledge that can be used to solve domain-specific problems. A key challenge that needs to be faced in this respect is the ability to automatically discover and integrate heterogeneous sensor data streams on the fly for applications to use them. To provide a domain-independent platform and take full benefits from semantic technologies, in this paper we present an Automated Complex Event Implementation System (ACEIS), which serves as a middleware between sensor data streams and smart city applications. ACEIS not only automatically discovers and composes IoT streams in urban infrastructures for users’ requirements expressed as complex event requests, but also automatically generates stream queries in order to detect the requested complex events, bridging the gap between high-level application users and low-level information sources. We also demonstrate the use of ACEIS in a smart travel planner scenario using real-world sensor devices and datasets.     

Frame-based ontological view for semantic integration

Semantic integration is crucial for successful collaboration between heterogeneous information systems. Traditional ontology-driven approaches rely on the availability of explicit ontologies. However, in most application domains, this prerequisite cannot be met. In order to address this issue, this paper investigates the theoretical foundation of ontologies and extends the traditional ontology concept to an ontological view concept. To explicitly and formally specify the ontological views, a Frame-based Ontological view Specification Language (FOSL) is proposed. This language is based on the frame knowledge representation paradigm and uses XML as the encoding. The ontological view driven semantic integration can be achieved based on the specifications. A proof-of-concept prototype environment has been implemented to achieve semantic integration based on ontological views specified with FOSL.     

Active semantic caching to optimize multidimensional data analysis in parallel and distributed environments

In this paper, we present a multi-query optimization framework based on the concept of active semantic caching. The framework permits the identification and transparent reuse of data and computation in the presence of multiple queries (or query batches) that specify user-defined operators and aggregations originating from scientific data-analysis applications. We show how query scheduling techniques, coupled with intelligent cache replacement policies, can further improve the performance of query processing by leveraging the active semantic caching operators. We also propose a methodology for functionally decomposing complex queries in terms of primitives so that multiple reuse sites are exposed to the query optimizer, to increase the amount of reuse. The optimization framework and the database system implemented with it are designed to be efficient irrespective of the underlying parallel and/or distributed machine configuration. We present experimental results highlighting the performance improvements obtained by our methods using real scientific data-analysis applications on multiple parallel and distributed processing configurations (e.g., single symmetric multiprocessor (SMP) machine, cluster of SMP nodes, and a Grid computing configuration).     

A multidimensional data model using the fuzzy model based on the semantic translation

With the rapid development of Web 2.0 sites such as Blogs and Wikis users are encouraged to express opinions about certain products, services or social topics over the web. There is a method for aggregating these opinions, called Opinion Aggregation, which is made up of four steps: Collect, Identify, Classify and Aggregate. In this paper, we present a new conceptual multidimensional data model based on the Fuzzy Model based on the Semantic Translation to solve the Aggregate step of an Opinion Aggregation architecture, which allows exploiting the measure values resulting from integrating heterogeneous information (including unstructured data such as free texts) by means of traditional Business Intelligence tools. We also present an entire Opinion Aggregation architecture that includes the Aggregate step and solves the rest of steps (Collect, Identify and Classify) by means an Extraction, Transformation and Loading process. This architecture has been implemented in an Oracle Relational Database Management System. We have applied it to integrate heterogeneous data extracted from certain high end hotels websites, and we show a case study using the collected data during several years in the websites of high end hotels located in Granada (Spain). With this integrated information, the Data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability and a high precision by means of interactive tools such as the dashboards.     

Energy-efficient migration techniques for cloud environment: a step toward green computing

The Journal of Supercomputing - The technology, cloud computing, in present days, is vastly used due to the services it provides and the ease with which they can be availed. The enormous...     

Wearable computing: toward humanistic intelligence

Over the past 20 years, wearable computing has emerged as the perfect tool for embodying humanistic intelligence (HI). HI is intelligence that arises when a human is part of the feedback loop of a computational process in which the human and computer are inextricably intertwined. It is common in the field of human-computer interaction to think of the human and computer as separate entities. (indeed, the term "HCI" emphasizes this separateness by treating the human and computer as different entities that interact.) However, in HI theory, we prefer not to think of the wearer and the computer with its associated I/O apparatus as separate entities. Instead, we regard the computer as a second brain and its sensory modalities as additional senses, in which synthetic synesthesia merges with the wearer's senses. When a wearable computer functions in a successful embodiment of HI, the computer uses the human's mind and body as one of its peripherals, just as the human uses the computer as a peripheral. This reciprocal relationship is at the heart of HI.     

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.     

SETL: A programmable semantic extract-transform-load framework for semantic data warehouses

In order to create better decisions for business analytics, organizations increasingly use external structured, semi-structured, and unstructured data in addition to the (mostly structured) internal data. Current Extract-Transform-Load (ETL) tools are not suitable for this “open world scenario” because they do not consider semantic issues in the integration processing. Current ETL tools neither support processing semantic data nor create a semantic Data Warehouse (DW), a repository of semantically integrated data. This paper describes our programmable Semantic ETL (SETL) framework. SETL builds on Semantic Web (SW) standards and tools and supports developers by offering a number of powerful modules, classes, and methods for (dimensional and semantic) DW constructs and tasks. Thus it supports semantic data sources in addition to traditional data sources, semantic integration, and creating or publishing a semantic (multidimensional) DW in terms of a knowledge base. A comprehensive experimental evaluation comparing SETL to a solution made with traditional tools (requiring much more hand-coding) on a concrete use case, shows that SETL provides better programmer productivity, knowledge base quality, and performance.