People see the world and convey their perception of it with narratives. In an information system context, stories are told and collected when the systems are developed. Requirements elicitation is... Show More

People see the world and convey their perception of it with narratives. In an information system context, stories are told and collected when the systems are developed. Requirements elicitation is largely dependent on communication between systems designers and users. Thus, stories have a significant impact on conceptualizing future users' needs. This paper presents a literature review on how stories and narratives have been considered in central IS literature. Narrative-theoretical parameters are used as a lens to analyze the literature. This shows that explicit discussion is non-existent, and the characteristics are considered partially. The result is a biased and narrow understanding of the informants' needs and wishes. This may be significant in the requirements because narratives are not as simple a form of communication as is usually assumed. It is proposed that better understanding narratives would equip systems analysts with an in-depth understanding about the nuances inherent in communication when communicating with users.

Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality... Show More

Based on the relationship between client load and overall system performance, the authors propose a sample-aware deep deterministic policy gradient model. Specifically, they improve sample quality by filtering out sample noise caused by the fluctuations of client load, which accelerates the model convergence speed of the intelligent tuning system and improves the tuning effect. Also, the hardware resources and client load consumed by the database in the working process are added to the model for training. This can enhance the performance characterization ability of the model and improve the recommended parameters of the algorithm. Meanwhile, they propose an improved closed-loop distributed comprehensive training architecture of online and offline training to quickly obtain high-quality samples and improve the efficiency of parameter tuning. Experimental results show that the configuration parameters can make the performance of the database system better and shorten the tuning time.

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the... Show More

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.

The adoption of empirical methods for secondary data analysis has witnessed a significant surge in IS research. However, the secondary data is often incomplete, skewed, and imbalanced at best.... Show More

The adoption of empirical methods for secondary data analysis has witnessed a significant surge in IS research. However, the secondary data is often incomplete, skewed, and imbalanced at best. Consequently, there is a growing recognition of the importance of empirical techniques and methodological decisions made to navigate through such issues. However, there is not enough methodological guidance, especially in the form of a worked case study that demonstrates the challenges of imbalanced datasets and offers prescriptive on how to deal with them. Using data on P2P money transfer services, this article presents a running example by analyzing the same dataset using several different methods. It then compares the outcomes of these choices and explicates the rationale behind some decisions such as inclusion and categorization of variables, parameter setting, and model selection. Finally, the article discusses certain regressions models such as weighted logistic regression and propensity matching, and when they should be used.

The acquisition and sharing of reviews have significant ramifications for the selection of crowdsourcing designs before mass production. This article studies the optimal decision of a brand... Show More

The acquisition and sharing of reviews have significant ramifications for the selection of crowdsourcing designs before mass production. This article studies the optimal decision of a brand enterprise regarding the acquisition/sharing of crowdsourcing design reviews in a supply chain. The authors consider an analytical model where the brand enterprise can privately acquire the manufacturer's review (MR) of crowdsourcing product designs and choose one of two information-sharing schemes—optional or mandatory sharing—to disclose MR to the key opinion leaders (KOLs), which help them to produce fans' reviews (FR). MR and FR integrate into the joint reviews (JR) that impact prospective consumers' purchase intention. The authors find that mandatory sharing significantly harms the brand enterprise's motivation to obtain MR, yet optional sharing is conducive to boosting JR on crowdsourcing designs. In addition, JR has a ceiling value, implying that excessively high FR and MR could not always enhance the effect of JR on crowdsourcing designs.

The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this... Show More

The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, the authors have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and 10 machine learning classifiers. It has been observed that the top three IDS models—KNeighbors, XGBoost, and AdaBoost—outperform binary-class classification with 99.49%, 99.14%, and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88%, and 96.66% accuracy.

In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage... Show More

In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage model that can write massive time-series data in a short time and can query and analyze the persistent time-series data for a long time. This paper constructs the RHTSDB (Redis-HBase Time Series Database) storage model based on Redis and HBase. RHTSDB uses the memory database Redis (Remote Dictionary Server) to cache massive time-series data, providing efficient data storage and query functions. HBase is used in RHTSDB for long-term storage of time-series data to realize their persistence. The paper designs a cold and hot separation mechanism for time-series data, where the infrequently accessed cold data are stored in HBase, and the frequently accessed and latest data are stored in Redis. Experiments verify that RHTSDB has apparent advantages over Apache IoTDB and HBase in data intake and query efficiency.