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Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID‐19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID‐19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre‐process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID‐19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision‐maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID‐19, CHD and hypertension, while 46.5% of people with COVID‐19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID‐19 disease. [ABSTRACT FROM AUTHOR]

Statistical reasoning was one of the earliest methods to draw insights from data. However, over the last three decades, association rule mining and online analytical processing have gained massive ground in practice and theory. Logically, both association rule mining and online analytical processing have some common objectives, but they have been introduced with their own set of mathematical formalizations and have developed their specific terminologies. Therefore, it is difficult to reuse results from one domain in another. Furthermore, it is not easy to unlock the potential of statistical results in their application scenarios. The target of this paper is to bridge the artificial gaps between association rule mining, online analytical processing and statistical reasoning. We first provide an elaboration of the semantic correspondences between their foundations, i.e., itemset apparatus, relational algebra and probability theory. Subsequently, we propose a novel framework for the unification of association rule mining, online analytical processing and statistical reasoning. Additionally, an instance of the proposed framework is developed by implementing a sample decision support tool. The tool is compared with a state-of-the-art decision support tool and evaluated by a series of experiments using two real data sets and one synthetic data set. The results of the tool validate the framework for the unified usage of association rule mining, online analytical processing, and statistical reasoning. The tool clarifies in how far the operations of association rule mining and online analytical processing can complement each other in understanding data, data visualization and decision making.

In a number of situations public sector information analysis uses to be performed empirically without any concrete procedure which should be based on the use of some models or even the use of collected data analysis tools. At the same time, the issue of data turning into conclusive information for a public institution does not benefit from an efficient development framework. On the contrary, the idea of systemized data analysis lacks completely in the case of most public institutions. This paper is intended as a case study of data analysis by means of an Online Analytical Processing (OLAP) system with real data from the Area Labour Inspectorate of Alba County (Romania) to prove both the efficiency and the need to create such a system. The aim is to prove the beneficial role of OLAP technology implementation in public institutions, at any level which may involve a managerial act.

Social platforms, such as Twitter, reveal much about the tastes of the public. Many studies focus on the content analysis of social platforms, which assists in product promotion and sentiment investigation. On the other hand, online analytical processing (OLAP) has been proven to be very effective for analyzing multidimensional structured data. The key purpose of applying OLAP to text messages, (e.g., tweets), called text OLAP, is to mine and construct the hierarchical dimension based on the unstructured text content. In contrast to the plain texts which text OLAP usually handles, the social media content includes a wealth of social relationship information which can be employed to extract a more effective dimensional hierarchy. In this paper, we propose a topic model called twitter hierarchical latent Dirichlet allocation (thLDA). Based on hierarchical latent Dirichlet allocation, thLDA aims to automatically mine the hierarchical dimension of tweets' topics, which can be further employed for text OLAP on the tweets. Furthermore, thLDA uses word2vec to analyze the semantic relationships of words in tweets to obtain a more effective dimension. We conduct extensive experiments on huge quantities of Twitter data and evaluate the effectiveness of thLDA. The experimental results demonstrate that it outperforms other current topic models in mining and constructing the hierarchical dimension of tweeters' topics.

A database management system is a constant application of science that provides a platfonn for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval teclmiques like indexing, que1y processing and concu!l'ency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructure.d enviromnent, pa1ticularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system. [ABSTRACT FROM AUTHOR]

Graph OLAP is a technology that generates aggregates or summaries of a large-scale graph based on the properties (or dimensions) associated with its nodes and edges, and in turn enables interactive analyses of the statistical information contained in the graph. To efficiently support these OLAP functions, a graph cube is widely used, which maintains aggregate graphs for all dimensions of the source graph. However, computing the graph cube for a large graph requires an enormous amount of time. While previous approaches have used the MapReduce framework to cut down on this computation time, the recently developed Spark environment offers superior computational performance. To leverage the advantages of Spark, we propose the GraphNaïve and GraphTDC algorithms. GraphNaïve sequentially computes graph cuboids for all dimensions in a graph, while GraphTDC computes them after first creating an execution plan. We also propose the Generate Multi-Dimension Table method to efficiently create a multidimensional graph table to express the graph. Evaluation experiments demonstrated that the GraphTDC algorithm significantly outperformed Spark SQL's built-in library DataFrame, as the size of graphs increased. [ABSTRACT FROM AUTHOR]

Materialized views are used as an alternative means for reducing the response time of analytical queries posed against a data warehouse. Since all views cannot be materialized and since optimal view selection is an NP-Hard problem, there is a need to select an appropriate subset of views for materialization that reduce the response times for analytical queries. This problem, referred to as view selection, is a widely studied problem in data warehousing. Several materialized view selection (MVS) algorithms exist that address the view selection problem, as a single objective optimization problem where the objective is to minimize the total cost of evaluating all the views (TVEC). This cost comprises two costs, i.e. the total cost of evaluation due to materialized views and the total cost of evaluation due to non-materialized views. Minimization of these two costs simultaneously would lead to the minimization of TVEC. In this paper, this bi-objective optimization problem, where the two costs are minimized simultaneously, has been solved using the Multi-Objective Genetic Algorithm (MOGA). The proposed MOGA based MVS algorithm selects the Top-K views from a multidimensional lattice with the purpose of achieving an optimal trade-off between the two aforementioned objectives. Materializing these selected Top-K views would reduce the response times for analytical queries and thereby would result in effective and efficient decision making. [ABSTRACT FROM AUTHOR]

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the 'distributed process mining' feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring 'doing the right things' within the workflow. An example of implementation is given, based on the case of slider manufacturer. [ABSTRACT FROM AUTHOR]

The BI (Business Intelligence) has gained great success during the last decade throughout the world to aid in decision support with the availability of necessary knowledge to reduce costs, increase revenues and minimize risks. The local market of Pakistan is still not very much aware of its benefits, except some multinationals which are using these tools since almost 7-8 years and earning more revenues and improved performances and a few others are under the process of implementation. The small and medium sized businesses of our local market are focused for the implementation of BI. The pros and cons are identified by analysing the BI tools being used by other large companies here in Pakistan and feasibility of these tools at small and medium enterprises is discussed so that they too may focus on their KPIs (Key Performance Indicator) to increase their performance level.

Pilot briefings, in their traditional form, drown pilots in a sea of information. Rather than unfocused swathes of air traffic management (ATM) information, pilots require only the information for their specific flight, preferably with an emphasis on the most important information. In this paper, we introduce the notion of ATM information cubes – in analogy to the well-established concept of Online analytical processing (OLAP) cubes in data warehousing. We propose a framework with merge and abstraction operations for the combination and summarization of the information in ATM information cubes to obtain management summaries of relevant information. To this end, we adopt the concept of semantic data container – a package of data items with a semantic description of the contents. The semantic descriptions then serve to hierarchically organise semantic containers along the dimensions of an ATM information cube. Leveraging this hierarchical organisation, a merge operation combines ATM information from individual semantic containers and collects the data items into composite containers. An abstraction operation summarises the data items within a semantic container, replacing individual data items with more abstract data items with summary information. [ABSTRACT FROM AUTHOR]

Recently, there are some online quantile algorithms that work on how to analyze the order statistics about the high-volume and high-velocity data stream, but the drawback of these algorithms is not scalable because they take the GK algorithm as the subroutine, which is not known to be mergeable. Another drawback is that they can't maintain the correctness, which means the error will increase during the process of the window sliding. In this paper, we use a novel data structure to store the sketch that maintains the order statistics over sliding windows. Therefore three algorithms have been proposed based on the data structure. And the fixed-size window algorithm can keep the sketch of the last W elements. It is also scalable because of the mergeable property. The time-based window algorithm can always keep the sketch of the data in the last T time units. Finally, we provide the window aggregation algorithm which can help extend our algorithm into the distributed system. This provides a speed performance boost and makes it more suitable for modern applications such as system/network monitoring and anomaly detection. The experimental results show that our algorithm can not only achieve acceptable performance but also can actually maintain the correctness and be mergeable. [ABSTRACT FROM AUTHOR]

Sustainable competitive advantage for universities could not be separated from the information technology. Actually, some universities have been implemented this information technology, especially virtual office applications. Eventhough, the virtual office applications were implemented, but preliminary research found that those applications used were Online Transaction Processing only, not Online Analytical Processing that can be used to make decision. Based on those explanations, this research had been done to explore and design the virtual office prototype application for universities. Three methods were used to conduct this reseach, which were explorative, validation, and application design. Finally, prototype applications of virtual office dashboard, lecturer dashboard and accreditation dashboard to improve sustainable competitive advantage for universities were presented as this study result.

The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.

OLAP (online analytical processing) is a key technology of business intelligence based on relational data. In big data era, people want to achieve high performance OLAP using a large cluster of ordinary nodes. However, the performance of such systems is a big challenge. Recently, many SQL on Hadoop systems have been proposed to address this challenge. We have seen a significant performance improvement of such systems. A survey of technology development of OLAP technologies was first provided. Then, a study of the performance of three representatives SQL on Hadoop systems was focused on. Based on the results, it is expected that such systems will play an very important role in the market of low cost OLAP analysis.

In view of problems of low accuracy and inadequate decision support existed in analysis and control of production cost of coal enterprises, the paper proposed a design scheme of production cost management and decision support. The scheme uses data market to collect the production cost in whole production range and make real-time collection and assignment for the production cost, and it uses technologies of online analytical processing and data mining to get law and trend hided in internal of the production cost, so as to provide decision support for decision-maker. The actual application shows that the scheme realizes real-time analysis and control for the production cost.