Traditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources. Frequent itemset mining (FIM) is one of the key algorithms in data mining and finds applications in a variety of domains; however, traditional algorithms do face problems in efficiently processing large and dynamic datasets. This research introduces a distributed incremental approximation frequent itemset mining (DIAFM) algorithm that tackles the mentioned challenges using shard-based approximation within the MapReduce framework. DIAFM minimizes the computational overhead of a program by reducing dataset scans, bypassing exact support checks, and incorporating shard-level error thresholds for an appropriate trade-off between efficiency and accuracy. Extensive experiments have demonstrated that DIAFM reduces runtime by 40–60% compared to traditional methods with losses in accuracy within 1–5%, even for datasets over 500,000 transactions. Its incremental nature ensures that new data increments are handled efficiently without needing to reprocess the entire dataset, making it particularly suitable for real-time, large-scale applications such as transaction analysis and IoT data streams. These results demonstrate the scalability, robustness, and practical applicability of DIAFM and establish it as a competitive and efficient solution for mining frequent itemsets in distributed, dynamic environments.

Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design.

Traditional land registry systems have been sub- ject to duplicated entries, manipulation of land documents, and possible risk of legal dispute in urban areas of Sindh, Pakistan. Growing urban population demands preservation of land documentation with efficacy, reliability and transparency. Blockchain based systems have emerged in recent years as a promising solution to ensure immutable and duplication free on- chain storage of records in the form encrypted hashes. In this study, we have demonstrated the integration of blockchain and artificial intelligence for ensuring digitized land record system for province Sindh. We developed the experimental and analytical framework comprising an Ethereum-based smart contract and machine learning based verification mechanism to eradicate prevalent hindrances on land record management. We leverage experimental methodology with real Pakistani naming norms, geographical distributions across ten Sindh districts, utilized the dataset of 2000 residential records, having induced inconsistent records (duplicates: 2.0%, fraudulent entries: 1.85%, ambiguous data: 3.0%). AI layer application was accomplished through a random forest classifier pre-screened records with 95.2% accuracy, preventing dubious entries from being registered on the blockchain. The implementation ensures maintaining immutable records, successfully prevented the duplication and transparency was achieved with public availability of data records. With improved security, transparency, and fraud resistance, the system architecture provides a significant pathway for digitizing urban land records.