High-utility itemset mining (HUIM) is an advanced problem of frequent itemset mining, considering the frequency of occurrence and quantitative criteria such as unit profit. Because HUIM can be applied to a broad spectrum of knowledge discovery work, various algorithmic improvements have been studied over the past two decades. On the other hand, limited efforts have been made to take advantage of hardware performance despite significant changes in hardware trends. This paper presents a novel parallelization method called DPHIM (Dynamic Parallelization for High-utility Itemset Mining). DPHIM dynamically decomposes a high-utility itemset mining task into subtasks to utilize logical parallelism and carefully assigns the subtasks and their related data to physical resources such as processing cores and nearby memory in a NUMA-aware manner. Through rigorous and diverse experiments, we found that DPHIM achieved speeds up to 72.7 times faster than the fully tuned serial execution, up to 23.5 times faster than static partitioning, and up to 2.5 times faster than the best case of alternative dynamic parallel executions for a variety of datasets and configurations on DRAM. We also demonstrated that DPHIM effectively worked on persistent memory; it offered similar thread scalability trends and was 1.1 to 2.4 times slower on persistent memory.

The manufacturing business field is accommodating a massive number of networked sensors, which are offering a new horizon of microscopic observability; every single piece of the production line is enabled to be monitored, transformed and organized into collective digital assets. Intensive analytics on the digital assets potentially offers new business solutions to improve the manufacturing productivity and secure the business continuity. For example, the capability of exhaustive production inspection would potentially remove the delivery of defective products. Another scenario is the capability of expeditiously identifying an impaired part in the production line, which would significantly reduce the yield loss. These business analytics work often hold the nature that they need to precisely identify a particular manufacturing event of interest (e.g., producing an intermediate material at a suspiciously faulty machine) and then trace back or forward every ancestor or descendant of the event thoroughly in the manufacturing pipeline spanning from the initial raw material to the final product. Yet, such microscopic business traceability has not been actively addressed in the technology community. This paper presents our experimental exploration of how to design relational database for manufacturing business analytics, which is technically differentiated by the involvement of microscopic business traceability from traditional business analytics. We have performed intensive experiments and cost-performance analyses to figure out the trade-offs associated with major options of physical database design with different benchmarks on a commercial database management system (DBMS) in an enterprise environment. The combination of columnar table stores and secondary indexes is recommended for manufacturing business analytics workloads, even though the pure use of columnar table stores is recommended for traditional analytics workloads. We believe that our experience would help those planning to work on microscopic business tractability in manufacturing and other fields.

High-utility itemset mining is a generalized problem of well-known frequent itemset mining, which considers not only the frequency of occurrence but also quantitative criteria such as unit profit. Because it can be applied to a wider spectrum of knowledge discovery work, various algorithmic improvements have been studied over the past two decades. On the other hand, limited efforts have been made to take advantage of hardware performance despite significant changes in hardware trends. This paper presents a novel parallelization method called DPHIM (Dynamic Parallelization for High-utility Itemset Mining). DPHIM dynamically decomposes the execution of high-utility itemset mining into subtasks in order to leverage logical data parallelism, and carefully assigns the subtasks and their related data to physical resources such as processing cores and nearby memory in the NUMA-aware manner. Our intensive and extensive experiments have confirmed that DPHIM performs up to 65.23 times faster than the fully-tuned serial execution, up to 23.54 times faster than static partitioning, and up to 2.51 times faster than the best case of alternative dynamic parallel executions for a variety of datasets and configurations on DRAM. As well, we have demonstrated that DPHIM effectively worked on persistent memory; it offered similar thread scalability trends and was 1.07 to 2.43 times slower on persistent memory.

High-utility itemset mining is a generalized problem of well-known frequent itemset mining, which considers not only the frequency of occurrence but also quantitative criteria such as unit profit. Because it can be applied to a wider spectrum of knowledge discovery work, various algorithmic improvements have been studied over the past two decades. On the other hand, limited efforts have been made to take advantage of hardware performance despite significant changes in hardware trends. This paper presents a novel parallelization method called DPHIM (Dynamic Parallelization for High-utility Itemset Mining). DPHIM dynamically decomposes the execution of high-utility itemset mining into subtasks in order to leverage logical data parallelism, and carefully assigns the subtasks and their related data to physical resources such as processing cores and nearby memory in the NUMA-aware manner. Our intensive and extensive experiments have confirmed that DPHIM performs up to 65.23 times faster than the fully-tuned serial execution, up to 23.54 times faster than static partitioning, and up to 2.51 times faster than the best case of alternative dynamic parallel executions for a variety of datasets and configurations on DRAM. As well, we have demonstrated that DPHIM effectively worked on persistent memory; it offered similar thread scalability trends and was 1.07 to 2.43 times slower on persistent memory.

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, this model's prosperous industrial use has been hindered by the problem of combinatorial explosion of patterns, which is the generation of too many redundant patterns, most of which may be useless to the user. We propose a novel model of closed periodic-frequent patterns that may exist in a temporal database to address this problem. Closed periodic-frequent patterns represent a concise lossless subset that uniquely preserves the complete information of all periodic-frequent patterns in a database. An efficient depth-first search algorithm, called Closed Periodic-Frequent Pattern Miner (CPFP-Miner), has been introduced to find all the database's desired patterns. Experimental results demonstrate that CPFP-Miner is not only memory, runtime, and energy-efficient, but also highly scalable. The usefulness of our model has also been shown with a case study on traffic congestion analytics.

Intra-query parallelism is a key for database software to offer acceptable responsiveness for data-intensive queries. Many researchers have studied how to achieve greater execution parallelism for database queries. Partitioning is a representative approach, which divides a query into multiple sub-tasks and executes them in parallel. However, given a new query, optimal division is not necessarily obvious. Database software utilizes heuristic rules or statistical information to decide how to divide the query before execution. As yet another approach to achieve execution parallelism, this paper presents out-of-order database execution (OoODE), a massively-parallel query execution method to offer significant speedup for database queries consistently. OoODE dynamically decomposes query work by making the best use of the exact knowledge of the potential execution parallelism for each operation ready to be performed during query execution. With OoODE, the database software is allowed to automatically squeeze out the execution parallelism that the query inherently holds. Hence, for a wide spectrum of queries, OoODE performs significantly faster than the serial (non-parallelized) execution, while it performs better than or comparably with alternative parallelizing methods without the need for dividing the query before execution. This paper presents the experiments that we conducted using the prototyped database software and demonstrates that OoODE is two to three orders of magnitude faster than the serial execution, whereas it is substantially (up to 2.07 times) faster than the best achievable case of partitioning. Besides, OoODE performs two to four orders of magnitude faster than major DBMSs.

Many update queries on a database can eventually degrade the structural efficiency of the database and result in lower performance. This phenomenon is called aging. On aged databases, conventional cost-based query optimizers could choose non-optimal query execution plan because they are not aging-aware and could not accurately estimate query execution cost.