Knowledge tracing (KT) aims to predict students' future performance based on their past learning activities. However, no one is perfect. Factors such as carelessness, fatigue, and stress often cause students to make mistakes on problems they have already mastered, leading to anomalies in their historical learning data. These anomalies disrupt inherent patterns in the data, misleading the KT model. Extracting cognitive patterns that accurately reflect students' knowledge mastery from such error-prone data remains a significant challenge. Against this background, this paper proposes a novel KT method named RoubstKT, inspired by educational measurement theory and frequency-based decomposition. A cognitive decoupling analyzer is proposed to decouple the student's cognitive pattern and random factors from the data through smoothing and subtraction operations, then recombine them using a gating mechanism or adaptive parameter fusion strategy. To more effectively diagnose students' knowledge mastery, we employ a decay-based attention mechanism that focuses on random behaviors at adjacent time steps. We conducted comprehensive experiments based on real-world datasets and targeted datasets with added random noise. The experimental results demonstrated the effectiveness of the proposed method.

Data warehouse and OLAP (Online Analytical Processing) are effective tools for decision support on traditional relational data and static multidimensional network data. However, many real-world multidimensional networks are often modeled as temporal multidimensional networks, where the edges in the network are associated with temporal information. Such temporal multidimensional networks typically cannot be handled by traditional data warehouse and OLAP techniques. To fill this gap, we propose a novel data warehouse model, named$\mathsf {Temporal{ }\; Graph{ }\; Cube}$, to support OLAP queries on temporal multidimensional networks. Through supporting OLAP queries in any time range, users can obtain summarized information of the network in the time range of interest, which cannot be derived by using traditional static graph OLAP techniques. We propose a segment-tree based indexing technique to speed up the OLAP queries, and also develop an index-updating technique to maintain the index when the temporal multidimensional network evolves over time. In addition, we also propose a novel concept called$\mathsf {similarity{ }\; of{ }\; snapshots}$which shows a strong correlation with the efficiency of indexing technique and can provide a good reference on the necessity of building the index. The results of extensive experiments on two large real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

The increase in the capacity of main memory coupled with the decrease in cost has fueled the development of in-memory database systems that manage data entirely in memory, thereby eliminating the disk I/O bottleneck. However, as we shall explain, in the Big Data era, maintaining all data in memory is impossible, and even unnecessary. Ideally we would like to have the high access speed of memory, with the large capacity and low price of disk. This hinges on the ability to effectively utilize both the main memory and disk. In this paper, we analyze state-of-the-art approaches to achieving this goal for in-memory databases, which is called as “Anti-Caching” to distinguish it from traditional caching mechanisms. We conduct extensive experiments to study the effect of each fine-grained component of the entire process of “Anti-Caching” on both performance and prediction accuracy. To avoid the interference from other unrelated components of specific systems, we implement these approaches on a uniform platform to ensure a fair comparison. We also study the usability of each approach, and how intrusive it is to the systems that intend to incorporate it. Based on our findings, we propose some guidelines on designing a good “Anti-Caching” approach, and sketch a general and efficient approach, which can be utilized in most in-memory database systems without much code modification.

Summary form only given. We propose a fast and efficient lossless codec of continuous-tone images, SPEDIC (simple predictor and edge detector based image codec), which is uniquely suitable for coding screen updates generated by multimedia applications in thin client computing systems. A codec for thin client computing should take account of the tradeoff between compression ratio and coding complexity, because screen update images in thin client computing should be sent from server to client in a timely fashion. We argue that by combining similar but simpler building blocks of the state-of-the-art methods, a little inferior compression ratio of JPEG-LS (the standard for lossless image compression today) can be attained with much lower coding complexity.