Optimization Techniques for RFID Complex Event Processing

One research crucial to wider adoption of Radio Frequency Identification （RFID） technology is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, RFID readings are usually of high volume and velocity, and have the attributes representing their reading objects, occurrence times and spots. Based on these characteristics and the Non-deterministic Finite Automata （NFA） implementation framework, this paper studies the performance issues of RFID complex event processing and proposes corresponding optimization techniques. Our techniques include： （1） taking advantage of negation events or exclusiveness between events to prune intermediate results, thus reduces memory consumption; （2） with different selectivities of complex events, purposefully reordering the join operations between events to improve overall efficiency, achieve higher stream throughput; （3） utilizing the slot-based or B＋-tree-based approach to optimizing the processing performance with the time window constraint. We present the analytical results of these techniques and validate their effectiveness through experiments.

Aggressive Complex Event Processing with Confidence over Out-of-Order Streams

In recent years, there has been a growing need for complex event processing （CEP）, ranging from supply chain management to security monitoring. In many scenarios events are generated in different sources but arrive at the central server out of order, due to the differences of network latencies. Most state-of-the-art techniques process out-of-order events by buffering the events until the total event order within a specified range can be guaranteed. Their main problems are leading to increasing response time and reducing system throughput. This paper aims to build a high performance out-of- order event processing mechanism, which can match events as soon as they arrive instead of buffering them till all arrive. A suffix-automaton-based event matching algorithm is proposed to speed up query processing, and a confidence-based accuracy evaluation is proposed to control the query result quality. The performance of our approach is evaluated through detailed accuracy and response time analysis. As experimental results show, our approach can obviously speed up the query matching time and produce reasonable query results.

Semantic Complex Event Detection System of Express Delivery Business with Data Support From Multidimensional Space

The express delivery induslry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition fi-amework to resolve such difficulty, which synthetically utilize intelligent inemet of things （IoT）, semantic web and complext event processing （CEP） technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support fTom multidimensional space.

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to harvesting.Pesticide residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly consumers.The residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in food.There is an obligation to provide a warning about this amount of pesticide use in farming.Previous technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential data.The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers.Overall,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

Digital Twin Framework and Its Application to Power Grid Online Analysis

Digital twin(DT)framework is introduced in the context of application for power grid online analysis.In the development process of a new power grid real-time online analysis system,an online analysis digital twin(OADT)has been implemented to realize the new online analysis architecture.The OADT approach is presented and its prominent features are discussed.The presentation,discussion,and performance testing are based on a large-scale grid network model(40K+buses),exported directly from the EMS system of an actual power grid.A plan to apply the OADT approach to digitize power grid dispatching rules is also outlined.

A New Solution Architecture for Online Power System Analysis

The current DSA system used in the dispatching control centers in China is a near real-time analysis system with response speed in the order of minutes.Based on a review of the state-of-the-art in online analysis and discussion of distributed data processing and computation architecture patterns,a new online analysis architecture is proposed.The primary goal of the new architecture is to increase the online analysis response speed to the order of seconds.A reference implementation of the proposed online analysis architecture to validate the feasibility of implementing the architecture and some performance testing results are presented.