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For aiding computer security experts in their study, log files are a crucial piece of information. Especially the time domain is very important for us because in most cases, timestamps are the only linking points between events caused by attackers, faulty systems or simple errors and their corresponding entries in log files. With the idea of storing and analyzing this log information in graph databases, we need a suitable model to store and connect timestamps and their events. This paper aims to find and evaluate different approaches how to store timestamps in graph databases and their individual benefits and drawbacks.

We analyse three different approaches, how timestamp information can be represented and stored in graph databases. For checking the models, we set up four typical questions that are important for log file analysis and tested them for each of the models. During the evaluation, we used the performance and other properties as metrics, how suitable each of the models is for representing the log files’ timestamp information. In the last part, we try to improve one promising looking model.

We come to the conclusion, that the simplest model with the least graph database-specific concepts in use is also the one yielding the simplest and fastest queries.

Limitations to this research are that only one graph database was studied and also improvements to the query engine might change future results.

In the study, we addressed the issue of storing timestamps in graph databases in a meaningful, practical and efficient way. The results can be used as a pattern for similar scenarios and applications.

Many synchronization approaches are based on low-level time capturing, causing a tight integration with the Media Access Control (MAC) layer. Alternatively, this study aims to present a hybrid approach combining both receiver–receiver and sender–receiver schemes to reduce the variation of two-way message exchange durations, in heavy-load networks. To achieve network-wide synchronization, a variant of Prim’s algorithm (Cormen et al., 2009) is used to build a spanning tree, guaranteeing the minimum number of ancestors and limited error propagation. The simulation results show that the proposed approach is very competitive with a set of the most-cited synchronization protocols. In addition, a new synchronization simulator SynSim was developed using C++ language

To achieve network-wide synchronization, a variant of Prim's algorithm (Cormen et al., 2009) is used to build a spanning tree, guaranteeing the minimum number of ancestors and limited error propagation.

Simulation results show that the proposed approach is very competitive with a set of the most-cited synchronization protocols. In addition, a new synchronization simulator SynSim was developed using in C++ language.

It can be concluded from the experiments that MDSP is suitable for WSNs especially if MAC layer timestamping is not possible. So, the mean delays synchronization protocol (MSDP) is suitable to achieve time synchronization in single-hop and multi-hop networks without the MAC layer timestamping in large wireless sensor network (WSN) deployments.

A future enhancement of MDSP could be switching between the traditional timestamping and the new proposed timestamping based on a given threshold, which is the number of nodes in the neighborhood and the load of the network. It will be also interesting to test it in a prototype. The proposed solution can be used in practice to implement the Time-division multiple access (TDMA) protocol in a WSN. In addition, the proposed simulator can be used in a computer network synchronization protocols course.

To the best of authors’ knowledge, this study’s contribution is original. In addition, the authors implemented a new synchronization simulator

Recently the advances in wireless communication technology and the popularity of portable computers have rendered mobile computing environments from which mobile users with battery‐operated palmtops can access the information via wireless channels, without space and time restriction. In mobile computing environments, mobile users cache the data items to use the bandwidth efficiently and improve the response time of mobile transactions. If the data items cached in mobile users are updated at the server, the server broadcasts an invalidation report for maintaining the cache consistency of mobile users. However, this method has an obstacle that does not guarantee the serializable execution of mobile transactions. The purpose of this paper is to propose the four types of reports for mobile transaction (FTR‐MT) method for ensuring the serializable execution of mobile transactions.

The paper describes the FTR‐MT method, which is composed of four types of algorithms, e.g. group report composition algorithm, immediate commit decision algorithm, cache consistency algorithm, and disconnection cache consistency algorithm. FTR‐MT method for improving the response time of mobile transactions makes a commit decision by using the four types of reports.

With the FTR‐MT method, mobile users can make a commit decision by using the four types of reports. The response time of mobile transactions can be reduced. Furthermore, the FTR‐MT method can improve the cache efficiency in the case that the disconnection of mobile users is longer than the broadcast interval of the window report.

This paper proposes a new method for guaranteeing the serializable execution of mobile transactions, called FTR‐MT, using four types of reports. Also, it can prevent the entire cache dropping, even though the disconnection of a mobile host is longer than the broadcast interval of a window report. Through the analytical model, this method is felt to be superior to other methods, in terms of the average response time and the commit rate of mobile transactions, and bandwidth usage.

The short lifespan of digital signatures presents a challenge to the long-term preservation of digitally signed records. It can undermine attempts to presume, verify or assess their authenticity. This paper aims to investigate the challenges of the expiration of digital signatures in the context of digital archiving.

The paper identifies requirements for the long-term preservation of digitally signed records and compares them with the existing approaches. The characteristics, operational procedures and requirements of the technologies used for digital signatures are combined with the archival requirements to design a new model.

The paper proposes a new model of a blockchain-based system, which can be combined with any digital archive to assist the process of long-term preservation of digitally signed records.

The proposed model offers a new alternative to the current practice in the long-term preservation of digitally signed records, such as periodic resigning procedures or periodic wrapping of digitally signed records with archival timestamps.

The proposed TrustChain 2.0 model is based on previous research conducted as part of the InterPARES Trust project. It builds on TrustChain 1.0 by including digital signature certificate chain validity information in a blockchain thus avoiding the issues concerning records confidentiality and privacy information disclosure. The paper contributes not only to the development of archival science but also shows archival institutions on how to approach long-term preservation of digitally signed records.

The purpose of this paper is to examine the blockchain as a trusted computing platform. Understanding the strengths and limitations of this platform is essential to execute large-scale real-world applications in blockchains.

This paper proposes several modifications to conventional blockchain networks to improve the scale and scope of applications.

Simple modifications to cryptographic protocols for constructing blockchain ledgers, and digital signatures for authentication of transactions, are sufficient to realize a scalable blockchain platform.

The original contributions of this paper are concrete steps to overcome limitations of current blockchain networks.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

In modern integrated modular avionics (IMA), it plays an important role to guarantee safety and reliability of the system. The safety and reliability of communication scheduling plays a crucial role in this IMA platforms-based network system. To verify the safety and reliability of the communication scheduling in this network, sufficient and typical test data must be generated to input into the network, obtain and evaluate the corresponding output.

To generate communication scheduling data, this paper presents an iterative communication scheduling data generation algorithm for a configured network of IMA platforms. First, the algorithm generates all possible communication schedules for the first timestamp. Then, constraint is introduced to decrease the quantity of communication schedules to improve the efficiency. The communication schedules are gradually extended to the second timestamp until the final timestamp, i.e. length of communication scheduling sequence.

To verify the efficiency and feasibility of the algorithm, a model is built based on the architecture analysis and design language (AADL) by mapping the correlation time of generated communication scheduling data into task properties. Schedulability is analyzed by loading this model into AADL Inspector. The simulation result illustrates that the proposed algorithm is efficient and feasible.

The proposed method can provide data support for communication scheduling test for the network of IMA platforms.

A constraint-based iterative communication scheduling data generation algorithm is proposed for the network of IMA platforms automatically.

This paper aims at investigating the possibility of effectively implementing the blockchain technology in the real estate environment, specifically applied to the Trust legal instrument in Dopo di Noi (After Us) project, which is intended to guarantee assistance to persons with severe disabilities.

The paper is focused on how to apply the blockchain to the tool of Trust, analyzing the main features and characteristics of this technology.

The paper proposes two potential solutions for managing the Trust tool in the real estate sector, specifically within the Dopo di Noi project. The first simpler proposal is based on timestamping application. The second one radically changes the classical Trust model and introduces an automatization level in the process.

The paper presents potential applications of the blockchain technology within the framework of Dopo di Noi project, which allows among other features, legal and tax facilitation for the institution of Trusts to benefit persons with severe disabilities.

This paper highlights the potentiality of the combination of the blockchain technology and the real estate environment and applies the blockchain technology to the Dopo di Noi project. Specifically, with the second solution, the paper proposes a platform that gathers, in a single network, various elements of the blockchain technology, such as timestamping, smart property, smart contract, and links them in order to provide services to persons with severe disabilities.

The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems.

This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy.

It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error.

The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available.

This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.

The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information.

The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods.

The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods.

In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency.

The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.