The Development of Logging Large-scale Management Information System

In this thesis, we present our works on resource management in large scale systems, especially for enterprise cluster and storage systems. Large-scale cluster systems become quite popular among a community of users by offering a variety of resources. Such systems require complex resource management schemes for multi-objective optimizations and should be specific to different system requirements. In addition, burstiness has often been found in enterprise workloads, being a key factor in performance degradation. Therefore, it is an extremely challenging problem of managing heterogeneous resources (e.g., computing, networking and storage) for such a large scale system under bursty conditions while providing performance guarantee and cost efficiency.

This paper has proposed a classification rule base mining algorithm combining the genetic algorithm and intuitionistic fuzzy-rough set for large-scale intuitionistic fuzzy information system. The algorithm has proposed innovatively the definitions and measurement metrics of completeness, interaction and compatibility describing the whole rule base, and constructed a multi-objective optimization model to optimize the population size of data sample, and used the intuitionistic fuzzy-rough set to reduce the attribute set of fuzzy information system, and used intuitionistic fuzzy similar class to extract large-scale intuitionistic fuzzy information system rules, and obtained an optimal rule base with the minimal size, configuration, generation time and storage space. A threshold control mechanism is used to evaluate the completeness, interaction and correlation of rule population and improves the robustness and flexibility of sample population optimization and rule base generation. The algorithm is verified by the real aircraft health data sets. The algorithm is validated by similar mature and effective algorithms in accuracy, time complexity using real data and has good robustness and adaptability to different size large-scale fuzzy information system.

This chapter introduces a systematic approach towards large-scale chain communication between autonomous organisations and professionals that cooperate to tackle social problems. This approach combines a specific chain perspective with a dynamic chain concept to better understand the complexities of large-scale social systems - with or without ICT – in a barely-manageable chain context. Many chain projects fail or falter and large-scale systems produce unexpected negative side-effects or even backfire. The example of the Dutch criminal law enforcement chain is used to explain how adversities and negative side effects can disrupt large-scale systems. This example stands as a model for other vital, large-scale systems, for instance identity management and health care. The underlying chain communication systems are important cornerstones of our future information society. Chain research at Utrecht University – now covering more than twenty chains in the Netherlands – has led to some valuable insights and breaking views that imply an ambitious agenda for public administration and information science.

This paper presents a method and concepts of a supporting tool for rapid prototyping of large-scale business information systems. Our method is based on the following guidelines: (1) small team of highly skilled members with combined skills, (2) prototype-based development of subsystems and the system as a whole, (3) brainstorming sessions always involving system analysts database and application designers, and user representatives (if needed), and (4) application generator providing for efficient prototype development by maximum automation of all design phases. The also presented application generator (AppGen) is based on standardization of functional and visual characteristics of an application, a library of high-level, coarse grained components, and a set of rules for model-to-application mapping enabling automatic application reconfiguration in case of changes in the data model.

-Enabling cryptographically enforced access controls for data hosted in untrusted cloud is attractive for many users and organizations. However, designing efficient cryptographically enforced dynamic access control system in the cloud is still challenging. In this paper, we propose Crypt-DAC, a system The rapid increase in textual data in navigation tasks for devices like GPS or smart assistants creates challenges for managing and storing data in large-scale systems. Data deduplication, which reduces storThis project presents AgriSafe, a secure and transparent land registration system designed specifically for the agricultural sector, integrating blockchain and artificial intelligence (AI) technologies. Recognizing the challenges posed by fraud and manipulation in traditional land registration systems, AgriSafe leverages blockchain to ensure immutability and tamper-proof storage of land ownership records. Advanced AI models—including logistic regression, support vector machines, and random forests—are employed to detect and eliminate fraudulent data before it is committed to the blockchain, ensuring only verified land data is stored. The system utilizes smart contracts for automated validation of land data, improving efficiency and accuracy. Furthermore, verified land information is securely stored on the Interplanetary File System (IPFS), with only the data hash stored on the blockchain, ensuring both security and accessibility. AgriSafe undergoes comprehensive evaluation, including AI model accuracy, blockchain scalability, and smart contract vulnerability assessment, delivering a robust, scalable, and fraud-resistant solution for agricultural land registration.age needs by eliminating duplicate data, offers a solution but raises security concerns. This paper introduces DEDUCT, a new method that combines cloud-side and client-side deduplication to achieve high data compression while protecting data privacy. Designed for devices with limited resources, such as IoT devices, DEDUCT includes lightweight preprocessing and safeguards against security risks like side-channel attacks. Testing on a navigation dataset shows that DEDUCT can compress data by up to 66%, significantly cutting storage costs while keeping data secure, making it an efficient choice for managing large-scale data systems. KeyWords: :Blockchain, Artificial Intelligence, Land Registration, Agriculture, Fraud Detection, Smart Contracts, IPFS, Logistic Regression, Support Vector Machine, Random Forest, Data Integrity, Scalability, AgriSafe.

SOC consensus control and analysis based on specified sampled-data for large-scale energy storage system

: A major attack can significantly reduce the capability to deliver services in large scale networked information systems. In this project, we have addressed the survivability of large-scale heterogeneous information systems which consist of various services provided over multiple interconnected networks with different technologies. The communications network portions of such systems are referred to as multi-networks. We specifically address the issue of survivability due to physical attacks that destroy links and nodes in multi-networks. The end goal is to support critical services in the face of a major attack by making optimum use of network resources while minimizing network congestion. This is an area which is little studied, especially for large-scale heterogeneous systems. In this report, we present an overview of our contribution in this area.

The article considers general issues of managing access control to resources of large-scale information systems (LSIS) from the standpoint of formal mathematical models. The properties inherent in LSIS and the requirements imposed on its security policy implementing methods and rules of access control are analyzed. The task is set to develop new models, methods and algorithms for managing access control in LSIS.

Surface Stabilization of O3-type Layered Oxide Cathode to Protect the Anode of Sodium Ion Batteries for Superior Lifespan.

Air Traffic Control System (ATCS) is a large-scale information system that spreads all over the world and is widely used (Zhang et al., Modern Air Traffic Management (in Chinese). Beijing University of Aeronautics and Astronautics Press, Beijing, 2005). Although it cannot represent the entire information system, its structure, functional composition and application methods can already reflect many basic characteristics of today’s information systems, especially large-scale information systems. In this chapter, OIT is applied to explain the behavior of ATCS for demonstrating the feasibility and practicality to investigate information systems.

The context of this work is the well studied dissemination of information in large scale distributed networks through pairwise interactions. This problem, originally called rumor mongering, and then rumor spreading has mainly been investigated in the synchronous model. This model relies on the assumption that all the nodes of the network act in synchrony, that is, at each round of the protocol, each node is allowed to contact a random neighbor. In this paper, we drop this assumption under the argument that it is not realistic in large scale systems. We thus consider the asynchronous variant, where at time unit, a single node interacts with a randomly chosen neighbor. We perform a thorough study of Tn the total number of interactions needed for all the n nodes of the network to discover the rumor. While most of the existing results involve huge constants that do not allow for comparing different protocols, we prove that in a complete graph of size n ≥ 2, the probability that T n > k for all k ≥ 1 is less than (1+2k(n−2)2/n)(1−2/n)(k−1). We also study the behavior of the complementary distribution of T n at point cE(T n ) when n tends to infinity for c ≠ 1. We end our analysis by conjecturing that when n tends to infinity, T n > E(T n ) with probability close to 0.4484.

A new approach to the verification and revision of large-scale mapping

Range queries are a very powerful tool in a wide range of data management systems and are vital to a multitude of applications. The hierarchy of structured overlay systems can be utilized in order to provide efficient techniques for processing them, resulting in the support of applications and techniques based on range queries in large-scale distributed information systems. On the other hand, due to the rapid development of the Web, applications based on the P2P paradigm gain more and more interest, having such systems started to evolve towards adopting standard database functionalities in terms of complex query processing support. This goes far beyond simple key lookups, as provided by standard distributed hashtables (DHTs) systems, which makes estimating the completeness of query answers a crucial challenge. Unfortunately, due to the limited knowledge and the usually best-effort characteristics, deciding about the completeness of query results, e.g., getting an idea when a query is finished or what amount of results is still missing, is very challenging. There is not only an urgent need to provide this information to the user issuing queries, but also for implementing sophisticated and efficient processing techniques based on them. In this chapter, the authors propose a method for solving this task. They discuss the applicability and quality of the estimations, present an implementation and evaluation for the P-Grid system, and show how to adapt the technique to other overlays. The authors also discuss the semantics of completeness for complex queries in P2P database systems and propose methods based on the notion of routing graphs for estimating the number of expected query answers. Finally, they discuss probabilistic guarantees for the estimated values and evaluate the proposed methods through an implemented system.

Establishing a highly sophisticated large-scale Traffic Information System (TIS) requires the creation and deployment of link travel time prediction models for large road networks. Due to the dimension of typical road networks and low coverage with Floating Cars (FC), data sets that can be used for prediction contain a large number of missing observations. Additionally, specifying prediction models for each link separately is impossible due to restrictions of both computational as well as modeling resources. This paper aims to improve the scalability of link travel time predictions by combining information from roads with similar characteristics. The Functional Road Class (FRC) is a widely accepted indicator for road similarity mainly based on static information from infrastructure planning. The coherence between the clustering introduced by the FRC and road dynamics measured by Floating Car Data (FCD) in the city of Vienna is discussed and analyzed. Clustering approaches that are based on indices characterizing speed measurement distributions are proposed as alternatives to the FRC system. It is demonstrated by way of examples that the new clustering is much more appropriate to provide predictions of link travel times.

In view of the characteristics with big data, high feature dimension, and dynamic for a large-scale intuitionistic fuzzy information systems, this paper integrates intuitionistic fuzzy rough sets and generalized dynamic sampling theory, proposes a generalized attribute reduction algorithm based on similarity relation of intuitionistic fuzzy rough sets and dynamic reduction. It uses dynamic reduction sampling theory to divide a big data set into small data sets and relative positive domain cardinality instead of dependency degree as decision-making condition, and obtains reduction attributes of big intuitionistic fuzzy decision information systems, and achieves the goal of extracting key features and fault diagnosis. The innovation of this paper is that it integrates generalized dynamic reduction and intuitionistic fuzzy rough set, and solves the problem of big data set which cannot be solved by intuitionistic fuzzy rough set. Taking an actual data as an example, the scientificity, rationality and effectiveness of the algorithm are verified from the aspects of stability, diagnostic accuracy, optimization ability and time complexity. Compared with similar algorithms, the advantages of the proposed algorithm for big data processing are confirmed.