High temporal fuzzy utility pattern analysis extracts patterns by considering temporal factors at which items appear, as well as expressing patterns in a linguistic way that humans can easily understand based on the fuzzy theory. However, in real world stream data environments, data often involve inherent uncertainty, such as inaccuracies in sensor data. Nevertheless, previous studies utilizing the temporal aspect for pattern analysis and fuzzy set theory do not explicitly account for such uncertainty or inaccuracies. Motivated by the limitation, we introduce a scalable method to extract high temporal fuzzy utility patterns within dynamic uncertain data streams. By incorporating uncertainty associated with items appearing in each transaction, our method extracts uncertainty-based high temporal fuzzy utility patterns that more accurately reflect real world conditions. The pattern expansion process is conducted efficiently with a list structure and various pruning methods. Experimental results indicate the proposed approach exhibits superior performance on runtime and memory usage compared to previous methods under diverse membership functions. Moreover, our method exhibits the highest scalability for increasing data volumes and maintains stable sensitivity to threshold variation while exhibiting the completeness and significance of extracted results. Evaluations under various uncertainty distributions show the superior performance of our approach, and effectiveness evaluations indicate contributions of proposed components. The case study involving the concept drifting evaluation indicates the applicability of our approach in various real-world environments with better performance. The implementation is available at https://github.com/sejongdmlab/ TFUN.

On the Internet of Things (IoT) environment, interconnected devices continuously share generated data in real time, typically collected with a specific purpose. To ensure efficiency, IoT technology must deliver only the most relevant insights into these devices. High utility pattern mining is a technique that extracts important knowledge, and there has been research on performance improvements to efficiently mine these patterns in dynamic environments. Although approaches with a prelarge concept have been introduced to mining high utility patterns in data deletion environments, the state-of-the-art method relies on inefficient data structures, making them unsuitable for real-time analysis with IoT data. To overcome these limitations, this article proposes a novel utility pattern mining approach with prelarge concept for dynamic IoT environments, where data is deleted because of sensor errors or storage constraints. The actual utilities of the prelarge patterns are maintained to skip the verification and improve communication delays. It can optimize processing time and memory consumption due to storing fewer large or prelarge patterns based on the actual utility. The proposed method operates in an efficient list-based manner, enabling effective search space pruning when the rescan condition is met and generating compact data structures through transaction merging. The experiments indicate that our algorithm is outstanding regarding processing time and scalability with minimal compromise in memory consumption compared with the existing methods, while extracting the exact patterns. Additionally, an analysis that replicates real IoT environments demonstrates that the proposed method is sufficiently applicable in real-world settings.

Utility-driven pattern analysis is a fundamental method for analyzing noteworthy patterns with high utility for diverse quantitative transactional databases. Recently, various approaches have emerged to handle large, dynamic database environments more efficiently by reducing the number of data scans and pattern expansion operations with the pre-large concept. However, existing pre-large-based high utility pattern mining methods either fail to handle real-time transaction modifications or require additional data scans to validate candidate patterns. In this paper, we propose a novel efficient utility-driven pattern mining algorithm using the pre-large concept for transaction modifications. Our method incorporates a single-scan-based framework through the management of actual utility values and discovers high utility patterns without candidate generation for efficient utility-driven dynamic data analysis in the modification environment. We compared the performance of the proposed method with state-of-the-art methods through extensive performance evaluation utilizing real and synthetic datasets. According to the evaluation results and a case study, the suggested method performs a minimum of 1.5 times faster than state-of-the-art methods alongside minimal compromise in memory, and it scaled well with increases in database size. Further statistical analyses indicate that the proposed method reduces the pattern search space compared to the previous method while delivering a complete set of accurate results without loss.

Pattern mining, one of the data analysis approaches, provides meaningful assistance for various business services, such as product recommendation and marketing. However, certain real-world data contain uncertain characteristics, and some business services want to consider the uncertainty of data. Uncertain pattern mining is an advanced technique for discovering more useful patterns from uncertainty-driven data with uncertain information about items. However, although many business services create and process incremental data in real-time, most of the previous uncertain pattern mining techniques have limitations in analyzing incremental data since they mainly focus on processing static data. To address the limitations, we present a list-based uncertain pattern mining method that effectively analyzes incremental uncertainty-driven data in real time by scanning stream data only once. In addition, uncertainty-driven data analytics can be executed efficiently due to the list structure that is effective in construction and mining. The tests of performance for runtime, memory consumption, and scalability are performed using real datasets and synthetic datasets, which illustrate that the suggested technique reveals outstanding performance compared to state-of-the-art algorithms. The additional case study evaluations with concept-drifting tests as well as accuracy and significance tests demonstrate the practical applications of the algorithm and the quality of the extracted results.

In a manufacturing factory, product lines are organized by several constituents and exhibit a profit value, i.e., income from products. Erasable patterns are less profitable patterns whose gain, i.e., the sum of product profits, does not exceed a user-defined threshold. Mining erasable patterns provides the necessary information to users who want to increase profits by erasing less profitable patterns. There are requirements for a method which efficiently manages uncertain databases in incremental environments to identify erasable patterns that consider uncertainty. Because our novel technique uses a list structure, it is more efficient at finding erasable patterns from incremental databases. Moreover, accumulated stream data should be handled efficiently to identify new useful patterns in both additional data and the existing data. In this article, an algorithm using a list-based structure is proposed to extract erasable patterns containing valuable knowledge from uncertain databases in real time with effective and productive performance. In order to derive erasable patterns from continuously accumulated stream databases, the structure efficiently manages the information gathered from the previous database. Extensive performance and pattern quality evaluations were conducted using real and synthetic datasets. The results show that the algorithm performs up to seven times faster than state-of-the-art erasable pattern mining algorithms on real datasets and scales adeptly on synthetic datasets while delivering reliable and significant result patterns.

In Long-Term Evolution (LTE) downlink control channel, a large number of blind decoding attempts are made, while the number of valid codewords is limited. The blind decoding results are then verified using a 16-bit cyclic redundancy check (CRC). However, even with the 16-bit CRC, the false alarm (FA) rate of such blind decoding is inevitably high. This paper investigates the problem of pruning of blind decoding results for reduction of the FA rate. To the best of our knowledge, the approach using a soft correlation metric (SCM) shows the best FA reduction performance among existing schemes. However, following the Bayes principle, we propose novel likelihood-based pruning that provides systematic balancing between the FA rate and the miss (MS) rate. Moreover, the simulation results show that the signal-to-noise ratio (SNR) gain of our proposed scheme is unbounded, with respect to the SCM-based scheme, in the independent and identically distributed (i.i.d.) Rayleigh fading channel. Moreover, the proposed scheme is shown to be less complex than the existing scheme. Finally, it is proved that, as SNR increases, the proposed approach has the decision error probability that approaches the minimum value yielding near-optimal contraction of Voronoi regions for pruning of blind decoding results.