Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is important in many applications, including fraud detection in credit card transactions and network intrusion detection. There are two general types of outlier detection: global and local. Global outliers fall outside the normal range for an entire dataset, whereas local outliers may fall within the normal range for the entire dataset, but outside the normal range for the surrounding data points. This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique. There are many LOF algorithms for a static data environment; however, these algorithms cannot be applied directly to data streams, which are an important type of big data. In general, local outlier detection algorithms for data streams are still deficient and better algorithms need to be developed that can effectively analyze the high velocity of data streams to detect local outliers. This paper presents a literature review of local outlier detection algorithms in static and stream environments, with an emphasis on LOF algorithms. It collects and categorizes existing local outlier detection algorithms and analyzes their characteristics. Furthermore, the paper discusses the advantages and limitations of those algorithms and proposes several promising directions for developing improved local outlier detection methods for data streams.

Serious games that integrate educational content with engaging gameplay mechanics hold promise for reducing cognitive load and increasing student motivation in STEM and health science education. This preliminary study presents the development and evaluation of the Medimon NASA Demo, a game-based learning prototype designed to teach undergraduate students about the musculoskeletal and visual systems—two critical domains in space medicine. Participants (n = 23) engaged with the game over a two-week self-regulated learning period. The game employed mnemonic-based characters, visual storytelling, and turn-based battle mechanics to reinforce medical concepts. Quantitative results demonstrated significant learning gains, with posttest scores increasing by an average of 23% and a normalized change of c = 0.4. Engagement levels were high across multiple dimensions of situational interest, and 74% of participants preferred the game over traditional formats. Qualitative analysis of open-ended responses revealed themes related to intrinsic appeal, perceived learning efficacy, interaction design, and cognitive resource management. While the game had minimal impact on short-term STEM career interest, its educational potential was clearly supported. These findings suggest that mnemonic-driven serious games like Medimon can effectively enhance engagement and learning in health science education, especially when aligned with real-world contexts such as space medicine.

In the field of big data, outlier detection is an important issue in many applications, such as communications, health, and network intrusion detection. Data streams present a challenge to the traditional outlier detection methods due to their unique characteristics, i.e., large volume and sequential structure. The Local Outlier Factor (LOF) is a popular algorithm used in anomaly detection to find outliers in a data stream. However, the LOF algorithm has a drawback. When a new point is added to the data stream, the algorithm needs to reprocess the measurement from the beginning of any change in the dataset. Additionally, LOF requires the whole dataset to be stored in memory. These issues were solved in the Grid Partition-based Local Outlier Factor (GP-LOF) algorithm. A new algorithm is introduced in this paper to improve the GP-LOF algorithm. It is based on the reachability distance measurement in LOF, and we name it Grid Partition-based Local Outlier Factor by Reachability distance (GP-LOFR) algorithm. GP-LOFR has slightly improved the accuracy rate of local outlier detection in our experiments with several real-world datasets.