Application of the Empirical Mode Decomposition On the Characterization and Forecasting of the Arrival Data of an Enterprise Cluster

Characterization and forecasting are two important processes in capacity planning. While they are closely related, their approaches have been different. In this research, a decomposition method called Empirical Mode Decomposition (EMD) has been applied as a preprocessing tool in order to bridge the input of both characterization and forecasting processes of the job arrivals of an enterprise cluster. Based on the facts that an enterprise cluster follows a standard preset working schedule and that EMD has the capability to extract hidden patterns within a data stream, we have developed a set of procedures that can preprocess the data for characterization as well as for forecasting. This comprehensive empirical study demonstrates that the addition of the preprocessing step is an improvement over the standard approaches in both characterization and forecasting. In addition, it is also shown that EMD is better than the popular wavelet-based decomposition in term of extracting different patterns from within a data stream

Investigating bus travel time and predictive models: a time series-based approach

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Enterprise Comprehensive Budget Informatization Management Based on Cloud Accounting and Blockchain Technology

With the continuous expansion of enterprise scale and economic development, the traditional comprehensive budget management lacks strategic guidance and information communication is becoming increasingly prominent, making it difficult to adapt to the needs of enterprise development in the new era. In response to this, research is conducted on modeling time series algorithms based on autoregressive moving average models, and a prediction model based on backpropagation neural networks is also established. And the error variance weighted average method is used to organically integrate two single prediction models to obtain a combined prediction model. A combination forecasting model is utilized to predict and analyze the budget of G Company. The analysis results ia applied to budget management. Finally, based on cloud accounting and blockchain technology, the enterprise budget management is optimized, and a comprehensive budget information management model for enterprises is constructed based on cloud accounting and blockchain technology. The experimental findings denote that after the model is put into use, a total of three projects complete 101.1%, 102.3%, and 106.0% of the budget, all of which are able to achieve the budget goals. The model can effectively improve the accuracy of enterprise budgeting and the effectiveness of budget execution

Developing Routinized Information Processing Capabilities for Operational Agility: Insights from China

Operational agility, which reflects the agile practices at business process level, is increasingly deemed as a significant determinant of business success in a turbulent business environment. Despite its importance, how operational agility can be attained is not answered by existing research. Drawing on the classic organization theory—information processing view of firms, the main contribution of this study is that it provides a process model of developing routinized information processing capabilities for operational agility in a turbulent business environment which fulfills this theoretical gap. It indicates the significant roles played by IT-enabled information processing networks and organizational controls during the process. It also identifies three routinized information processing capabilities including information sensitivity, information fluidity, and information decomposability. This is achieved by conducting a case study of Haier, one of the largest producers of household appliances in China. This paper concludes with a discussion of potential theoretical and practical contributions

EMD based hybrid models in short term traffic speed forecast

Short-term freeway traffic speed prediction is essential to improving mobility and safety. It has been a challenging, yet unresolved issue. Traffic speed prediction can be applied to enhance the intelligent freeway traffic management and control for applications as operational and regulation planning. For example, with more reliable traffic speed prediction, the Advanced Traveler Information System (ATIS) can provide travelers with travel time information which allows travelers to arrange their schedule accordingly. Moreover, traffic managers can use the prediction information to deploy various traffic management strategies so as to increase the efficiency of the whole network. In this research, a hybrid empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) (or equivalently EMD-ARIMA) approach is developed to predict the short-term traffic speed on freeways. In general, there are three stages in the hybrid EMD-ARIMA forecasting framework. The first step is the EMD stage which decomposes the freeway traffic speed series data into a number of intrinsic mode function (IMF) components and a residue. The second stage is to find the appropriate ARIMA model for each IMF and residue, then make predictions based on the appropriate ARIMA model. The third stage is to combine the prediction results of each IMF and residue to get the predictions. Two experiments are conducted thereafter. The experimental results indicate that the proposed hybrid EMD-ARIMA framework is capable to predict short-term freeway traffic speed with high accuracy

Temporal Mining for Distributed Systems

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions

Agent-based inter-organizational systems in advanced logistics operations

“Agent-based Inter-organizational Systems (ABIOS) in Advanced Logistics Operations” explores the concepts, the design, and the role and impact of agent-based systems to improve coordination and performance of logistics operations. The dissertation consists of one conceptual study and three empirical studies. The empirical studies apply various research methods such as a multiple-case study research, coordination mechanism design, and predictive analytics using big data. The conceptual study presents a theoretical exploration and synthesis explaining the demand for inter-organizational systems (IOS) and the corresponding IOS functionalities. The first empirical study presents a multiple-case study exploring real