Towards a Global Real-Time Enterprise

Author(s):  
Peter Ibach ◽  
Miroslaw Malek ◽  
Gerrit Tamm

Frictionless interoperation of business processes even across enterprise boundaries, complete up-to-date view of the overall status, and full remote control over the business parameters for individuals in charge – this is the holy grail of a “global real-time enterprise”. Yet a vision, a number of enabling technologies brought us closer to accomplishing the challenges: Sensing the position of mobile objects and processes status, distributing the status information with “zero latency”, discovering it according to specific demands across organisation boundaries, providing and securing uniform service-oriented access to all kinds of entities ranging from smart items to business processes, and aggregating the overwhelming variety of elementary services to form high-value composite services. In this chapter, we overview the enabling technologies that drive the development and further discuss market factors, security and privacy concerns, and standardization processes that have to be considered. Then we propose our SEMALON approach – the SEMAntic LOcation Network – intended as a basic infrastructure for discovery and composition of location based services. Finally we describe our experiences from a case study implementation – the NOMADS Campus – which is a distributed spatial information system for our campus at Humboldt University Berlin.

2021 ◽  
pp. 0309524X2199826
Author(s):  
Guowei Cai ◽  
Yuqing Yang ◽  
Chao Pan ◽  
Dian Wang ◽  
Fengjiao Yu ◽  
...  

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.


2021 ◽  
Vol 10 (7) ◽  
pp. 489
Author(s):  
Kaihua Hou ◽  
Chengqi Cheng ◽  
Bo Chen ◽  
Chi Zhang ◽  
Liesong He ◽  
...  

As the amount of collected spatial information (2D/3D) increases, the real-time processing of these massive data is among the urgent issues that need to be dealt with. Discretizing the physical earth into a digital gridded earth and assigning an integral computable code to each grid has become an effective way to accelerate real-time processing. Researchers have proposed optimization algorithms for spatial calculations in specific scenarios. However, a complete set of algorithms for real-time processing using grid coding is still lacking. To address this issue, a carefully designed, integral grid-coding algebraic operation framework for GeoSOT-3D (a multilayer latitude and longitude grid model) is proposed. By converting traditional floating-point calculations based on latitude and longitude into binary operations, the complexity of the algorithm is greatly reduced. We then present the detailed algorithms that were designed, including basic operations, vector operations, code conversion operations, spatial operations, metric operations, topological relation operations, and set operations. To verify the feasibility and efficiency of the above algorithms, we developed an experimental platform using C++ language (including major algorithms, and more algorithms may be expanded in the future). Then, we generated random data and conducted experiments. The experimental results show that the computing framework is feasible and can significantly improve the efficiency of spatial processing. The algebraic operation framework is expected to support large geospatial data retrieval and analysis, and experience a revival, on top of parallel and distributed computing, in an era of large geospatial data.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Liwen Zhang ◽  
Xianwen Fang ◽  
Chifeng Shao ◽  
Lili Wang

2017 ◽  
Vol 43 (4) ◽  
pp. 142-146 ◽  
Author(s):  
Ugo FALCHI

The final goal of this paper was to fix a brief summary on the status of geographic information in Italy due to the technological steps and national regulations. The acquisition, processing and sharing of spatial data has experienced a significant acceleration thanks to the development of computer technology and the acknowledgment of the need for standardization and homogenization of information held by pub­lic authorities and individuals. The spatial data represents the essential knowledge in the management and development of a territory both in terms of planning for safety and environmental prevention. In Italy there is an enormous heritage of spatial information which is historically affected by a problem of consistency and uniformity, in order to make it often contradictory in its use by the public decision-maker and private par­ties. The recent history of geographic information is characterized by a significant effort aimed at optimiz­ing this decisive technical and cultural heritage allowing the use of it to all citizens in a logic of sharing and re-use and may finally represent a common good available to all.


2013 ◽  
Vol 701 ◽  
pp. 440-444
Author(s):  
Gang Liu ◽  
Peng Tao Liu ◽  
Bao Sheng He

Sand production is a serious problem during the exploitation of oil wells, and people put forward the concept of limited sand to alleviate this problem. Oil production with limited sanding is an efficient mod of production. In order to complete limited sand exploitation, improve the productivity of oil wells, a real-time sand monitoring system is needed to monitor the status of wells production. Besides acoustic sand monitoring and erosion-based sand monitoring, a vibration-based sand monitoring system with two installing styles is proposed recently. The paper points out the relationships between sand monitoring signals collected under intrusive and non-intrusive installing styles and sanding parameters, which lays a good foundation for further study and actual sand monitoring in oil field.


2013 ◽  
Vol 86 (11) ◽  
pp. 2939-2965 ◽  
Author(s):  
Raffaele Conforti ◽  
Marcello La Rosa ◽  
Giancarlo Fortino ◽  
Arthur H.M. ter Hofstede ◽  
Jan Recker ◽  
...  

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.


2019 ◽  
Vol 25 (5) ◽  
pp. 948-971
Author(s):  
Kanana Ezekiel ◽  
Vassil Vassilev ◽  
Karim Ouazzane ◽  
Yogesh Patel

Purpose Changing scattered and dynamic business rules in business workflow systems has become a growing problem that hinders the use and configuration of workflow-based applications. There is a gap in the existing research studies which currently focus on solutions that are application specific, without accounting for the universal logical dependencies between the business rules and, as a result, do not support adaptation of the business rules in real time. The paper aims to discuss this issue. Design/methodology/approach To tackle the above problems, this paper adopts a bottom-up approach, which puts forward a component model of the business process workflows and then adds business rules which have clear logical semantics. This allows incremental development of the workflows and semantic indexing of the rules which govern them during the initial acquisition. Findings The paper introduces an event-driven model for development of business workflows which is purely logic-based and can be easily implemented using an object-oriented technology, together with a model of the business rules dependencies which supports incremental semantic indexing. It also proposes a two-level inference mechanism as a vehicle for controlling the business process execution and the process of adaptation of the business rules at real time based on propagating the dependencies. Research limitations/implications The framework is strictly logical and completely domain-independent. It allows to account both synchronous and asynchronous triggering events as well as both qualitative and quantitative description of the conditions of the rules. Although our primary interest is to apply the framework to the business processes typical in the construction industry we believe our approach has much wider potential due to its strictly logical formalization and domain independence. In fact it can be used to control any business processes where the execution is governed by rules. Practical implications The framework could be applied to both large business process modelling tasks and small but very dynamic business processes like the typical digital business processes found in online banking or e-Commerce. For example, it can be used for adjusting security policies by adding the capability to adapt automatically the access rights to account for additional resources and new channels of operation which can be very interesting ion both B2C and B2B applications. Social implications The potential scope of the impact of the research reported here is linked to the wide applicability of rule-based systems in business. Our approach makes it possible not only to control the execution of the processes, but also to identify problems in the control policies themselves from the point of view of their logical properties – consistency, redundancies and potential gaps in the logics. In addition to this, our approach not only increases the efficiency, but also provides flexibility for adaptation of the policies in real time and increases the security of the overall control which improves the overall quality of the automation. Originality/value The major achievement reported in this paper is the construction of a universal, strictly logic-based event-driven framework for business process modelling and control, which allows purely logical analysis and adaptation of the business rules governing the business workflows through accounting their dependencies. An added value is the support for object-oriented implementation and the incremental indexing which has been possible thanks to the bottom-up approach adopted in the construction of the framework.


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