Development of a New KPI for the Economic Quantification of Six Big Losses and Its Implementation in a Cyber Physical System

The purpose of this work is to develop a new Key Performance Indicator (KPI) that can quantify the cost of Six Big Losses developed by Nakajima and implements it in a Cyber Physical System (CPS), achieving a real-time monitorization of the KPI. This paper follows the methodology explained below. A cost model has been used to accurately develop this indicator together with the Six Big Losses description. At the same time, the machine tool has been integrated into a CPS, enhancing the real-time data acquisition, using the Industry 4.0 technologies. Once the KPI has been defined, we have developed the software that can turn these real-time data into relevant information (using Python) through the calculation of our indicator. Finally, we have carried out a case of study showing our new KPI results and comparing them to other indicators related with the Six Big Losses but in different dimensions. As a result, our research quantifies economically the Six Big Losses, enhances the detection of the bigger ones to improve them, and enlightens the importance of paying attention to different dimensions, mainly, the productive, sustainable, and economic at the same time.

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A Novel Key-value based Real-time Data Management Framework for Ship Integrated Power Cyber-Physical System

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) ◽

10.1109/isgt-asia.2019.8881124 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chengya Shang ◽

Xianqiang Bao ◽

Lijun Fu ◽

Li Xia ◽

Xinghua Xu ◽

...

Keyword(s):

Data Management ◽

Real Time ◽

Physical System ◽

Cyber Physical System ◽

Time Data ◽

Management Framework ◽

Real Time Data

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Prediction Capabilities for Cyber Physical Vehicles

International Journal of Cyber-Physical Systems ◽

10.4018/ijcps.2019010104 ◽

2019 ◽

Vol 1 (1) ◽

pp. 45-70

Author(s):

Laszlo Z. Varga

Keyword(s):

Real Time ◽

Physical System ◽

Autonomous Vehicles ◽

Route Planning ◽

Traffic Information ◽

Cyber Physical System ◽

Time Data ◽

Real Time Data ◽

Online Traffic ◽

State Of The Environment

Cyber physical systems open new ground in the automotive domain. Autonomous vehicles will try to adapt to the changing environment, and decentralized adaptation is a new type of issue that needs to be studied. This article investigates the effects of adaptive route planning when real-time online traffic information is exploited. Simulation results show that if the agents selfishly optimize their actions, then in some situations, the cyber physical system may fluctuate and sometimes the agents may be worse off with real-time data than without real-time data. The proposed solution to this problem is to use anticipatory techniques, where the future state of the environment is predicted from the intentions of the agents. This article concludes with this conjecture: if simultaneous decision-making is prevented, then intention-aware prediction can limit the fluctuation and help the cyber physical system converge to the Nash equilibrium, assuming that the incoming traffic can be predicted.

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Parallelisation of a Cache-Based Stream-Relation Join for a Near-Real-Time Data Warehouse

Electronics ◽

10.3390/electronics9081299 ◽

2020 ◽

Vol 9 (8) ◽

pp. 1299

Author(s):

M. Asif Naeem ◽

Habib Khan ◽

Saad Aslam ◽

Noreen Jamil

Keyword(s):

Real Time ◽

Data Warehouse ◽

Cost Model ◽

Parallel Execution ◽

Stream Data ◽

Time Data ◽

Sales Data ◽

Master Data ◽

Real Time Data ◽

Two Phases

Near real-time data warehousing is an important area of research, as business organisations want to analyse their businesses sales with minimal latency. Therefore, sales data generated by data sources need to reflect immediately in the data warehouse. This requires near-real-time transformation of the stream of sales data with a disk-based relation called master data in the staging area. For this purpose, a stream-relation join is required. The main problem in stream-relation joins is the different nature of inputs; stream data is fast and bursty, whereas the disk-based relation is slow due to high disk I/O cost. To resolve this problem, a famous algorithm CACHEJOIN (cache join) was published in the literature. The algorithm has two phases, the disk-probing phase and the stream-probing phase. These two phases execute sequentially; that means stream tuples wait unnecessarily due to the sequential execution of both phases. This limits the algorithm to exploiting CPU resources optimally. In this paper, we address this issue by presenting a robust algorithm called PCSRJ (parallelised cache-based stream relation join). The new algorithm enables the execution of both disk-probing and stream-probing phases of CACHEJOIN in parallel. The algorithm distributes the disk-based relation on two separate nodes and enables parallel execution of CACHEJOIN on each node. The algorithm also implements a strategy of splitting the stream data on each node depending on the relevant part of the relation. We developed a cost model for PCSRJ and validated it empirically. We compared the service rates of both algorithms using a synthetic dataset. Our experiments showed that PCSRJ significantly outperforms CACHEJOIN.

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Development of a New Green Indicator and Its Implementation in a Cyber–Physical System for a Green Supply Chain

Sustainability ◽

10.3390/su12208629 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8629 ◽

Cited By ~ 1

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Royo ◽

Juan Carlos Sánchez ◽

Lisbeth del Carmen Ng Corrales

Keyword(s):

Supply Chain ◽

Energy Consumption ◽

Real Time ◽

Physical System ◽

Machine Tools ◽

Industry 4.0 ◽

Performance Indicator ◽

Green Supply Chain ◽

Cyber Physical System ◽

Key Performance Indicator

This work investigates Industry 4.0 technologies by developing a new key performance indicator that can determine the energy consumption of machine tools for a more sustainable supply chain. To achieve this, we integrated the machine tool indicator into a cyber–physical system for easy and real-time capturing of data. We also developed software that can turn these data into relevant information (using Python): Using this software, we were able to view machine tool activities and energy consumption in real time, which allowed us to determine the activities with greater energy burdens. As such, we were able to improve the application of Industry 4.0 in machine tools by allowing informed real-time decisions that can reduce energy consumption. In this research, a new Key Performance Indicator (KPI) was been developed and calculated in real time. This KPI can be monitored, can measure the sustainability of machining processes in a green supply chain (GSC) using Nakajima’s six big losses from the perspective of energy consumption, and is able to detect what the biggest energy loss is. This research was implemented in a cyber–physical system typical of Industry 4.0 to demonstrate its applicability in real processes. Other productivity KPIs were implemented in order to compare efficiency and sustainability, highlighting the importance of paying attention to both terms at the same time, given that the improvement of one does not imply the improvement of the other, as our results show.

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Real-time Web Search Framework for Performing Efficient Retrieval of Data

Journal of information and organizational sciences ◽

10.31341/jios.45.1.13 ◽

2021 ◽

Vol 45 (1) ◽

pp. 287-308

Author(s):

Falah Al-akashi ◽

Diana Inkpen

Keyword(s):

Real Time ◽

Web Search ◽

Focal Point ◽

Relevant Information ◽

Life Cycles ◽

Specific Information ◽

Time Data ◽

Time System ◽

Real Time System ◽

Real Time Data

With the rapidly growing amount of information on the internet, real-time system is one of the key strategies to cope with the information overload and to help users in finding highly relevant information. Real-time events and domain-specific information are important knowledge base references on the Web that frequently accessed by millions of users. Real-time system is a vital to product and a technique must resolve the context of challenges to be more reliable, e.g. short data life-cycles, heterogeneous user interests, strict time constraints, and context-dependent article relevance. Since real-time data have only a short time to live, real-time models have to be continuously adapted, ensuring that real-time data are always up-to-date. The focal point of this manuscript is for designing a real-time web search approach that aggregates several web search algorithms at query time to tune search results for relevancy. We learn a context-aware delegation algorithm that allows choosing the best real-time algorithms for each query request. The evaluation showed that the proposed approach outperforms the traditional models, in which it allows us to adapt the specific properties of the considered real-time resources. In the experiments, we found that it is highly relevant for most recently searched queries, consistent in its performance, and resilient to the drawbacks faced by other algorithms

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Online Processing of End-User Data in Real-Time Data Warehousing

Improving Knowledge Discovery through the Integration of Data Mining Techniques - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-8513-0.ch002 ◽

2015 ◽

pp. 13-31

Author(s):

M. Asif Naeem ◽

Noreen Jamil

Keyword(s):

Real Time ◽

Cost Model ◽

Main Memory ◽

Stream Data ◽

Time Data ◽

Online Processing ◽

Master Data ◽

Real Time Data ◽

User Data ◽

Resource Aware

Stream-based join algorithms are a promising technology for modern real-time data warehouses. A particular category of stream-based joins is a semi-stream join where a single stream is joined with a disk based master data. The join operator typically works under limited main memory and this memory is generally not large enough to hold the whole disk-based master data. Recently, a seminal join algorithm called MESHJOIN (Mesh Join) has been proposed in the literature to process semi-stream data. MESHJOIN is a candidate for a resource-aware system setup. However, MESHJOIN is not very selective. In particular, MESHJOIN does not consider the characteristics of stream data and its performance is suboptimal for skewed stream data. This chapter presents a novel Cached-based Semi-Stream Join (CSSJ) using a cache module. The algorithm is more appropriate for skewed distributions, and we present results for Zipfian distributions of the type that appear in many applications. We conduct a rigorous experimental study to test our algorithm. Our experiments show that CSSJ outperforms MESHJOIN significantly. We also present the cost model for our CSSJ and validate it with experiments.

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TinyLFU-Based Semi-Stream Cache Join for Near-Real-Time Data Warehousing

10.21203/rs.3.rs-944044/v1 ◽

2022 ◽

Author(s):

M. Asif Naeem ◽

Wasiullah Waqar ◽

Farhaan Mirza ◽

Ali Tahir

Keyword(s):

Real Time ◽

Data Warehousing ◽

Cost Model ◽

Research Problem ◽

Daily Basis ◽

Stream Data ◽

Time Data ◽

Business Decisions ◽

Real Time Data ◽

Modern Era

Abstract Semi-stream join is an emerging research problem in the domain of near-real-time data warehousing. A semi-stream join is basically a join between a fast stream (S) and a slow disk-based relation (R). In the modern era of technology, huge amounts of data are being generated swiftly on a daily basis which needs to be instantly analyzed for making successful business decisions. Keeping this in mind, a famous algorithm called CACHEJOIN (Cache Join) was proposed. The limitation of the CACHEJOIN algorithm is that it does not deal with the frequently changing trends in a stream data efficiently. To overcome this limitation, in this paper we propose a TinyLFU-CACHEJOIN algorithm, a modified version of the original CACHEJOIN algorithm, which is designed to enhance the performance of a CACHEJOIN algorithm. TinyLFU-CACHEJOIN employs an intelligent strategy which keeps only those records of $R$ in the cache that have a high hit rate in S. This mechanism of TinyLFU-CACHEJOIN allows it to deal with the sudden and abrupt trend changes in S. We developed a cost model for our TinyLFU-CACHEJOIN algorithm and proved it empirically. We also assessed the performance of our proposed TinyLFU-CACHEJOIN algorithm with the existing CACHEJOIN algorithm on a skewed synthetic dataset. The experiments proved that TinyLFU-CACHEJOIN algorithm significantly outperforms the CACHEJOIN algorithm.

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A Cached-Based Stream-Relation Join Operator for Semi-Stream Data Processing

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2016070102 ◽

2016 ◽

Vol 12 (3) ◽

pp. 14-31 ◽

Cited By ~ 2

Author(s):

M. Asif Naeem ◽

Imran Sarwar Bajwa ◽

Noreen Jamil

Keyword(s):

Data Processing ◽

Cost Model ◽

Stream Data ◽

Time Data ◽

Join Algorithm ◽

Real Time Data ◽

Join Algorithms ◽

The Cost ◽

Novel Algorithm ◽

Better Than

Stream-based join algorithms got a prominent role in the field of real-time data warehouses. One particular type of stream-based joins is a semi-stream join where a single stream is joined with a disk -based relation. Normally the size of this disk-based relation is large enough and cannot be fit into memory, available for join operator. Therefore, the relation is loaded into memory in partitions. A well-known join algorithm called MESHJOIN (Mesh Join) has been presented in the literature to process semi-stream data. However, the algorithm has some limitations. In particular, MESHJOIN does not consider the characteristics of stream data and therefore does not perform well for skewed stream data. This article introduces the concept of caching and based on that presents a novel algorithm called Cached-based Stream-Relation Join (CSRJ). The algorithm exploits skewed distributions more appropriately, and the authors present results for Zipfian distributions of the type that appear in many applications. They test their algorithm using synthetic, TPC-H and real datasets. Their experiments show that CSRJ forms significantly better than MESHJOIN. They also drive the cost model for their algorithm and based on that they tune the algorithm.

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