This paper discuss a project design,which a intelligent manage and maintenance on small or middle LAN in computer’s laboratoryand,and how to carry out.When we use some characteristic of Windows 2000 Server,remote boot(e.g.diskless) and a few secondary software to apply the design adequately,we can take a load off our mind on so many clients.In the end,we have a good evaluation and some results.
How to determine the poor students is the premise and foundation of the poor students' aid work in the major colleges and universities. Although after many years of exploration and improvement, colleges and universities throughout the country have also formed a set of their own identification methods for poor students, but the implementation effect is not ideal. The identification of poor students is still a difficult problem for poor students in colleges and universities. This paper holds that in the process of determining the poor students, especially the index of relative poverty should be a series of index system which synthesizes many factors, but the data performance of these factors is fuzzy, so we can combine big data, Using Fuzzy Cluster Analysis to establish a complete method to determine Poverty The model can be used to accurately identify the poor students.
Feature selection or attribute reduction is an important data preprocessing technique for dimensionality reduction in machine learning and data mining. In this paper, a novel feature selection ensemble learning algorithm is proposed based on Tsallis entropy and Dempster–Shafer evidence theory (TSDS). First, an improved correlation criterion is used to obtain the relevant feature based on Tsallis entropy. A forward sequential approximate Markov blanket is then defined to eliminate the redundant feature. An ensemble learning is employed to achieve approximately optimal global feature selection, which can acquire the feature subsets from different perspectives. Finally, by fusing all the feature subsets, the improved evidence theory approach is utilized to gain the final feature subset. To verify the effectiveness of TSDS, nine datasets from two different domains are used in the experimental analysis. The experimental results demonstrate that the proposed algorithm can select feature subset more effectively and enhance the classification performance significantly.
The role of XML in data exchange is evolving from one of merely conveying the structure of data to one that also conveys its semantics. In particular, several proposals for key and foreign key constraints have recently appeared, and aspects of these proposals have been adopted within XMLSchema.In this paper, we examine the problem of checking keys and foreign keys in XML documents using a validator based on SAX. The algorithm relies on an indexing technique based on the paths found in key definitions, and can be used for checking the correctness of an entire document (bulk checking) as well as for checking updates as they are made to the document (incremental checking). The asymptotic performance of the algorithm is linear in the size of the document or update. Furthermore, experimental results demonstrate reasonable performance.
Business intelligence (BI) transforms large volumes of data within modern organizations into actionable insights for informed decision-making. Recently, large language model (LLM)-based agents have streamlined the BI workflow by automatically performing task planning, reasoning, and actions in executable environments based on natural language (NL) queries. However, existing approaches primarily focus on individual BI tasks such as NL2SQL and NL2VIS. The fragmentation of tasks across different data roles and tools lead to inefficiencies and potential errors due to the iterative and collaborative nature of BI. In this paper, we introduce DataLab, a unified BI platform that integrates a one-stop LLM-based agent framework with an augmented computational notebook interface. DataLab supports a wide range of BI tasks for different data roles by seamlessly combining LLM assistance with user customization within a single environment. To achieve this unification, we design a domain knowledge incorporation module tailored for enterprise-specific BI tasks, an inter-agent communication mechanism to facilitate information sharing across the BI workflow, and a cell-based context management strategy to enhance context utilization efficiency in BI notebooks. Extensive experiments demonstrate that DataLab achieves state-of-the-art performance on various BI tasks across popular research benchmarks. Moreover, DataLab maintains high effectiveness and efficiency on real-world datasets from Tencent, achieving up to a 58.58% increase in accuracy and a 61.65% reduction in token cost on enterprise-specific BI tasks.
Fueled by novel technologies capable of producing massive amounts of data, scientists have been faced with an explosion of information that must be rapidly analyzed and integrated with other data to form hypotheses and create knowledge. Success in science now hinges critically on the availability of computational and data management tools to meet these challenges.
Michael Stonebraker recently argued that the traditional database concept of “one size fits all” which provides a unique strategy to manage data in all different applications, is no longer applicable in the database market. Nowhere is this truer than with scientific data. Scientific data differs significantly from business data, for which current database technology has been developed.
My research is focused on tree-structured scientific data management, one type of scientific data that models an inherently hierarchical process or object. Due to its hierarchical structure, XML has become a common scientific data format (http://xml.gsfc.nasa.gov). However, XML's standard query languages, XPath and XQuery, are not well suited for many scientific applications, in particular, computational linguistics and phylogenetic tree applications. I have spent a significant portion of my research efforts to efficiently support these two types of scientific applications. Specifically, I have studied and summarized commonly used operations (queries) on the data, analyzed why XML techniques cannot be easily applied, and designed and implemented data management systems for these two types of applications.
Mobile Edge Computing (MEC) is a key technology of IoTs and 5G networks and presents many challenges (such as resource discovery, resource allocation, computation offloading and transmission power design) that need to be addressed. Previous works studying on improving the performance of MEC system may only optimize these issues separately or just jointly optimized part of them. In this paper, we propose a Discovery-Allocation-Transmission-Offloading (DATO) strategy by taking all these aspects into consideration with discovery order to minimize energy consumption. Discovery strategy indicates when to suspend edge node (EN) discovery and perform computation offloading. Transmission strategy specifies the optimal power used to transmit the data. The offloading strategy tells the portion of data offloaded to the EN for execution. The discovery order demonstrates the sequence of EN searching. This problem is formulated as a stochastic sequential decision-making problem and Dynamic Programming (DP) is used to achieve the optimal scheme. Numerical results show the effectiveness of our proposed strategy.
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