Knowledge and Information Systems最新文献

The incremental recommendation involves updating existing models by extracting information from interaction data at current time-step, with the aim of maintaining model accuracy while addressing limitations including parameter dependencies and inefficient training. However, real-time user interaction data is often afflicted by substantial noise and invalid samples, presenting the following key challenges for incremental model updating: (1) how to effectively extract valuable new knowledge from interaction data at the current time-step to ensure model accuracy and timeliness, and (2) how to safeguard against the catastrophic forgetting of long-term stable preference information, thus preserving the model’s sensitivity during cold-starts. In response to these challenges, we propose the Incremental Recommendation with Stable Latent Side-information Updating (SIIFR). This model employs a side-information augmenter to extract valuable latent side-information from user interaction behavior at time-step T, thereby sidestepping the interference caused by noisy interaction data and acquiring stable user preference. Moreover, the model utilizes rough interaction data at time-step (T+1), in conjunction with existing side-information enhancements to achieve incremental updates of latent preferences, thereby ensuring the model’s efficacy during cold-start. Furthermore, SIIFR leverages the change rate in user latent side-information to mitigate catastrophic forgetting that results in the loss of long-term stable preference information. The effectiveness of the proposed model is validated and compared against existing models using four popular incremental datasets. The model code can be achieved at: https://github.com/LNNU-computer-research-526/FR-sii.

Process mining algorithms essentially reflect the execution behavior of events in an event log for conformance checking, model discovery, or enhancement. Domain experts have developed several process mining algorithms based on theoretical frameworks such as linear integer programming, heuristics, and genetic algorithms, region-based and semantic-based approaches. The idea is to generate insightful representations of these processes of information systems to enable process mining practitioners to gain insight into their systems. Recently, there has been a shift toward semantic-based approaches for process mining since they not only discover enhanced models but also emphasize context. To this effect, this paper conducts a comprehensive review of 30 articles on semantic process mining techniques. It was found that 44.7% of all works used semantics for process discovery, 23.7% for model enhancement, and conformance checking was the least with 10.5%. We further indicate the benefits and contributions of these methods to process mining. Challenges, opportunities, and prospective future research areas are also discussed.

The safety of the civil aviation system has been of increasing concern with several accidents in recent years. It is urgent to put forward a precise accident prediction model, which can systematically analyze safety from the perspective of accident mechanism to enhance training accuracy. Furthermore, the predictive model is critical for stakeholders to identify risk and implement the proactive safety paradigm. In this work, to mitigate casualties and economic losses arising from aviation accidents and improve system safety, the focus is on predicting the aircraft damage severity, the injury/death severity, and the flight phases in the sequence of identifying event risk sources. This work establishes a multi-task deep convolutional neural network (MTCNN) learning framework to accomplish this goal. An innovative prediction rule will be developed to refine prediction results from two approaches: handling imbalanced classes and Bayesian optimization. By comparing the performance of the proposed multi-task model with other single-task machine learning models with ten-fold cross-validation and statistical testing, the effectiveness of the developed model in predicting aviation accident severity and flight phase is demonstrated.

Many fields of research use parallelized and distributed computing environments, including astronomy, earth science, and bioinformatics. Due to an increase in client requests, service providers face various challenges, such as task scheduling, security, resource management, and virtual machine migration. NP-hard scheduling problems require a long time to implement an optimal or suboptimal solution due to their large solution space. With recent advances in artificial intelligence, deep reinforcement learning (DRL) can be used to solve scheduling problems. The DRL approach combines the strength of deep learning and neural networks with reinforcement learning’s feedback-based learning. This paper provides a comprehensive overview of DRL-based scheduling algorithms in distributed systems by categorizing algorithms and applications. As a result, several articles are assessed based on their main objectives, quality of service and scheduling parameters, as well as evaluation environments (i.e., simulation tools, real-world environment). The literature review indicates that algorithms based on RL, such as Q-learning, are effective for learning scaling and scheduling policies in a cloud environment. Additionally, the challenges and directions for further research on deep reinforcement learning to address scheduling problems were summarized (e.g., edge intelligence, ideal dynamic task scheduling framework, human–machine interaction, resource-hungry artificial intelligence (AI) and sustainability).

Many hierarchical methods for community detection in multicolored networks are capable of finding clusters when there are interslice correlation between layers. However, in general, they aggregate all the links in different layer treating them as being equivalent. Therefore, such aggregation might ignore the information about the relevance of a dimension in which the node is involved. In this paper, we fill this gap by proposing a hierarchical classification-based Louvain method for interslice-multicolored networks. In particular, we define a new node centrality measure named Attractivity to describe the inter-slice correlation that incorporates within and across-dimension topological features in order to identify the relevant dimension. Then, after merging dimensions through a frequential aggregation, we group nodes by their relational and attribute similarity, where attributes correspond to their relevant dimensions. We conduct an extensive experimentation using seven real-world multicolored networks, which also includes comparison with state-of-the-art methods. Results show the significance of our proposed method in discovering relevant communities over multiple dimensions and highlight its ability in producing optimal covers with higher values of the multidimensional version of the modularity function.

Question Answering (QA) systems provide accurate answers to questions; however, they lack the ability to consolidate data from multiple sources, making it difficult to manage complex questions that could be answered with additional data retrieved and integrated on the fly. This integration is inherent to Situational Data Integration (SDI) approaches that deal with dynamic requirements of ad hoc queries that neither traditional database management systems, nor search engines are effective in providing an answer. Thus, if QA systems include SDI characteristics, they could be able to return validated and immediate information for supporting users decisions. For this reason, we surveyed QA-based systems, assessing their capabilities to support SDI features, i.e., Ad hoc Data Retrieval, Data Management, and Timely Decision Support. We also identified patterns concerning these features in the surveyed studies, highlighting them in a timeline that shows the SDI evolution in the QA domain. To the best of your knowledge, this study is precursor in the joint analysis of SDI and QA, showing a combination that can favor the way systems support users. Our analyses show that most of SDI features are rarely addressed in QA systems, and based on that, we discuss directions for further research.

A favorable business environment plays a crucial role in facilitating the high-quality development of a modern economy. In order to enhance the credibility and efficiency of business environment evaluation, this paper proposes a hybrid storage blockchain-based query efficiency enhancement method for business environment evaluation. Currently, most blockchain systems store block data in key-value databases or file systems with simple semantic descriptions. However, such systems have a single query interface, limited supported query types, and high storage overhead, which leads to low performance. To tackle these challenges, this paper proposes a query efficiency enhancement method based on hybrid storage blockchain. Firstly, data are stored in a hybrid data storage architecture combining on-chain and off-chain. Additionally, relational semantics are added to block data, and three index mechanisms are designed to expedite data access. Subsequently, corresponding query efficiency enhancement algorithms are designed based on the query types that are applicable to the aforementioned three index mechanisms, further refining the query processing. Finally, a comprehensive authentication query is implemented on the blockchain for the light client, and the user can verify the soundness and integrity of the query results. Experimental results on three open datasets show that the method proposed in this paper significantly reduces storage overhead, has shorter query latency for three different query types, and improves retrieval performance and verification efficiency.

The high dimensionality of text data is a challenging issue that requires efficient methods to reduce vector space and improve classification accuracy. Existing filter-based methods fail to address the redundancy issue, resulting in the selection of irrelevant and redundant features. Information theory-based methods effectively solve this problem but are not practical for large amounts of data due to their high time complexity. The proposed method, termed semantic similarity-aware feature selection and redundancy removal (SS-FSRR), employs joint mutual information between the pairs of semantically related terms and the class label to capture redundant features. It is predicated on the assumption that semantically related terms imply potentially redundant ones, which can significantly reduce execution time by avoiding sequential search strategies. In this work, we use Word2Vec’s CBOW model to obtain semantic similarity between terms. The efficiency of the SS-FSRR is compared to six state-of-the-art competitive selection methods for categorical data using two traditional classifiers (SVM and NB) and a robust deep learning model (LSTM) on seven datasets with 10-fold cross-validation, where experimental results show that the SS-FSRR outperforms the other methods on most tested datasets with high stability as measured by the Jaccard’s Index.