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.

Our workshop aims to provide a platform for both academic and industrial professionals engaged in the analysis and retrieval of cross-data from diverse perspectives, with a particular emphasis on wearable and ambient sensors, lifelog cameras, social networks, and surrounding sensors.Despite numerous studies exploring individual viewpoints, there remains a significant gap in the analysis and retrieval of cross-data to maximize benefits for humanity.Additionally, challenges such as data security and distributed learning for cross-modal model training and inference arise when dealing with large and distributed datasets.We invite researchers to contribute to this initiative, with the overarching goal of fostering the development of a smart and sustainable society through the efficient utilization of intelligent cross-data analysis and retrieval techniques.

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.

A geo-referenced time series is a crucial form of spatiotemporal data. Useful information that can empower the users to achieve economic development is hidden in this series. When confronted with this problem, researchers modeled this series as a transactional database and discovered various user interest-based patterns. Since transactional databases disregard the items’ sequential ordering information, existing studies are inadequate to find interesting patterns in the data of those applications, where the items’ sequential ordering needs to be considered. With this motivation, this paper first presents a new data transformation technique that converts geo-referenced time series data into a geo-referenced sequential database that preserves the items’ sequential occurrence information. Second, this paper presents a novel model of geo-referenced frequent sequential patterns that may exist in a database. Third, a novel neighborhood-aware exploration technique has been presented to effectively reduce the search space and the computational cost of finding the desired patterns. Finally, we present an efficient algorithm to find all desired patterns in a database. Experimental results demonstrate that the proposed algorithm is efficient. We demonstrate the usefulness of our patterns with a case study, which involves finding congestion patterns in road network data.

Periodic-frequent pattern mining is a vital knowledge discovery technique that aims to find all regularly occurring patterns in a temporal database. Previous studies focused on developing CPU-centric algorithms by disregarding the speedups offered by the GPUs. Furthermore, existing GPU-based frequent pattern mining algorithms cannot be employed to find periodic-frequent patterns because they ignore the items' temporal occurrence information in the database, and the multi-threaded sum-reduction technique cannot be employed to determine the periodicity of a pattern in a database. With this motivation, this paper proposes an efficient GPU-accelerated depth-first search algorithm, GPU Periodic Frequent-Miner (gPF-Miner), to find the desired patterns. Our algorithm employs a novel flattened array structure to effectively record the temporal occurrence information of every item in a database. Our algorithm also introduces a new multi-threaded parallelization technique to calculate the support and periodicity of a pattern in a GPU. This technique’s best and worst-case time complexities are O(1) and O(n), where n represents the data size. Experimental results demonstrate that gPF-Miner outperforms the existing CPU-based and naive GPU-based algorithms by a vast margin.

A geo-referenced time series database represents the data generated by a set of fixed locations (or spatial items) observing a particular phenomenon over time. This data hides valuable information that can help users progress in their social and economic lives. This paper presents a new model of Geo-referenced Periodic-Frequent Patterns (GPFPs) that might be in these databases. A GPFP is a set of frequently occurring items close to each other and seen in a database at regular intervals. Three constraints have been used to figure out how interesting a pattern is in a geo-referenced time series database: maximum distance (maxDist), minimum support (minSup), and maximum periodicity (maxPer). The maxDist controls how far apart the items in a pattern can be. The minimum number of times a pattern must appear in the data is controlled by the minSup. Lastly, the maxPer variable specifies how many times a pattern must repeat before it is considered periodic in the data. Each pattern that satisfies these three requirements will be returned. An effective method known as the Geo-referenced Periodic-Frequent Pattern-Miner (GPFP-Miner) has been proposed to discover all GPFPs included inside a geo-referenced time series database. GPFP-Miner uses an innovative, smart depth-first search approach to uncover required patterns efficiently. The findings of the experiments support the contention that the proposed algorithm is effective. In addition, we present two case studies in which we utilise our methodology to extract meaningful information from databases pertaining to air pollution and traffic congestion.

Crucial information that can empower the users to achieve socioeconomic development lies hidden in big temporal databases. Previous studies employed partial periodic pattern mining techniques to find all regularly occurring patterns in the data. Unfortunately, these techniques consumes too much energy as they often produce too many patterns most of which may be uninteresting to the user. This paper tackles this problem by introducing two new types of patterns, namely maximal partial periodic patterns and closed partial periodic patterns patterns, which represent a concise set of all partial periodic patterns that may exist in the data. Two depth-first search algorithms were also described to find the desired patterns. Experimental results demonstrate that our algorithms are both runtime, memory, and energy efficient. The usefulness of our patterns was also demonstrated with a case-study on air pollution data.

High utility itemset mining is an important model in data mining. It involves discovering all itemsets in a quantitative transactional database that satisfy a user-specified minimum utility (minUtil) constraint. MinUtil controls the minimum value that an itemset must maintain in a database. Since the model evaluates an itemset’s interestingness using only the minUtil constraint, it implicitly assumes that all items in the database have similar utility values. However, some items have high utility, while others may have relatively low utility in a database. If minUtil is set too high, the user will miss all itemsets containing low utility items. To find itemsets that involve both high and low utility items, minUtil has to be set very low. However, this may cause a combinatorial explosion as the items with high utility may combine with others in all possible ways. This dilemma is called the low utility item problem. This paper proposes a flexible model of relative high utility itemset to address this problem. We introduce a new null-invariant measure, called utility ratio, to evaluate the interestingness of an itemset in the database. We also present a fast single scan algorithm to find all desired itemsets in the database. Experimental results demonstrate that the proposed algorithm is efficient. Finally, a case study on Yahoo! JAPAN retail data shows that the proposed model is useful.

Currently, air pollution has become the tremendous problem humankind has ever faced. Countries governments have struggled with conflicting demands from industries benefits and people/natural health. Scientists have investigated to find solutions that can balance these demands. Along with these directions, this research introduces a convenient and economical solution for estimating PM2.5 at the current time and predicting PM2.5 in a short- and medium-term period just by using images captured from personal devices (e.g., smartphones, cameras, lifelog cameras). The proposed method aims to leverage the association between urban nature (e.g., street greenness, street building), urban traffic (e.g., the volume of vehicles), and air pollution (e.g., PM2.5) to discover a set of periodic-frequent patterns and to build the PM2.5 estimation model. The estimated PM2.5, together with a set of patterns, is utilized to predict the PM2.5 in the short-term future. Evaluation running on different datasets collected from India and Vietnam shows the productivity of the proposed method.

Partial periodic pattern mining is an important model in data mining with many real-world applications. However, this model’s successful industrial application was hindered by the problem of combinatorial explosion of patterns, which involves generating too many patterns, most of which might be redundant or uninteresting to the user. Furthermore, the problem of combinatorial explosion increases the memory, runtime, and the energy requirements of a mining algorithm. This paper aims to tackle this challenging problem by proposing a novel model of maximal partial periodic pattern that may exist in a database. We also present a new tree structure and a pattern-growth algorithm, called Maximal Partial Periodic Pattern-growth (max3P-growth), to find all desired patterns effectively. Experimental results demonstrate that the proposed model prunes many redundant patterns, and the max3P-growth is efficient and scalable. Finally, we show the usefulness of our model with a case study on traffic congestion analytics.

Spatial High Utility Itemset Mining (SHUIM) is an important knowledge discovery technique with many real-world applications. It involves discovering all itemsets that satisfy the user-specified m inimum u tility (minUtil) i n a q uantitative spatiotemporal database. The popular adoption and the successful industrial application of this technique have been hindered by the following two limitations: (i) Since the rationale of SHUIM is to find all itemsets that satisfy the minUtil constraint, it often produces too many patterns, most of which may be redundant or uninteresting to the user. (ii) Specifying a right minUtil value is an open research problem in SHUIM. This paper tackles these two problems by proposing a novel model of top-k spatial high utility itemsets that may exist in a database. A new constraint, called dynamic minimum utility (dMinUtil), was explored to reduce the search space effectively. This constraint is based on a greedy search, where we raise its value through five thresholdraising strategies. An efficient single scan algorithm that employs depth-first search to find all top-k spatial high utility itemsets was also presented in this paper. Experimental results demonstrate that our algorithm is memory and runtime efficient. We will also demonstrate the usefulness of our algorithm with two real-world case studies.

Nowadays, resolving chaotic traffic situations, which usually link to traffic congestion, is an essential need. It poses many risks to commuters like traffic accidents, especially during bad weather situations. Besides, owing to the exponential growth of IoT technologies, it is easier than ever to collect a huge amount of urban sensing data. Therefore, building a system to anticipate congestion from the collected data could enhance public safety and give traffic police forces enough time to handle traffic flows in potentially dangerous areas. Moreover, if we can discover patterns in which predicted congestion usually happens, we can build reaction plans with various alert codes. They create dynamic risk maps that can provide useful knowledge to both authorities and travelers to make rescue and travel plans effectively. This paper proposes a novel framework to address these problems. The proposed framework employs the Enhanced-Fusion-3DCNN deep learning model to predict future long-term traffic congestion on a particular mesh-code at a particular time instance. The predicted traffic congestion data is later transformed into a temporal database and feed to the maximal periodic-frequent pattern algorithm to identify the sets of mesh-code in which regular congestion may happen in the predicted data. Experimental results on real-world traffic congestion data demonstrate that the proposed framework is efficient.

Finding Spatial High Utility Itemsets (SHUIs) in a spatiotemporal database is a challenging problem of great importance in many real-world applications. Most previous works focused on the sequential discovery of SHUIs in a database running on a single machine. Consequently, these works are not suitable for big data (or cloud-based) applications as they suffer from the scalability and fault tolerant problems. This paper proposes several novel pruning techniques to reduce the search space and present a more flexible distributed algorithm to find all desired itemsets from the database using Spark in-memory computing architecture. Our algorithm inherits several advantages of Spark, including low communication cost, fault tolerance, and high scalability. Experimental results demonstrate that the proposed algorithm has good scalability and performance on very large databases. Finally, we present a real-world navigation application in which SHUIs generated from the traffic congestion data have been employed to recommend alternative routes to the users.

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.

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, the successful industrial application of this model has been hindered by the problem of combinatorial explosion of patterns, that is the generation of too many redundant patterns, most of which may be useless to the user. To address this problem, this paper proposes a novel model of maximal periodic- frequent pattern that may exist in a temporal database. A new pattern-growth algorithm, called Maximum Periodic-Frequent Pattern-growth (maxPFP-growth), has also been introduced to efficiently find all desired patterns in the data. Experimental results demonstrate that maxPFP-growth is not only memory and runtime efficient, but also highly scalable as well. The usefulness of our model has also been demonstrated with a case study on traffic congestion analytics.

Frequent pattern mining is an important model in data mining. It involves finding all patterns in a transactional database that satisfy the user-specified minimum support (minSup) constraint. The minSup controls the minimum number of transactions that a pattern must cover in a transactional database. Since only minSup is used to evaluate a pattern's interestingness, the frequent pattern model implicitly assumes that spatial information of the items will not impact the interestingness of a pattern in the database. This assumption limits the applicability of the frequent pattern model in many real-world applications. It is because patterns whose items are close to each other are typically more attractive to the user than the patterns whose items are far from each other in a coordinate system. With this motivation, this paper proposes a novel model of frequent spatial pattern that may exist in a spatiotemporal database. An efficient pattern-growth algorithm, called Frequent Spatial Pattern-growth (FSP-growth), has also been presented to mine all desired patterns in a database. Experimental results demonstrate that our algorithm is efficient. The usefulness of the proposed patterns has also been shown with a real-world application.

Finding partial periodic patterns in very large databases is a challenging problem of great importance in many real-world applications. Most previous work focused on finding these patterns in temporal (or transactional) databases and did not recognize the spatial characteristics of items. In this paper, we propose a more flexible model of partial periodic spatial pattern that may be present in spatiotemporal database. Three constraints, maximum inter-arrival time(maxIAT), minimum period-support(minPS) and maximum distance(maxDist), have been employed to determine the interestingness of a pattern in a spatiotemporal database. The maxIAT controls the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The minPS controls the minimum number of periodic occurrences of a pattern within the data. The maxDist controls the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned. An efficient algorithm, called SpatioTemporal-Equivalence CLAss Transformation (ST-ECLAT), has also been described to discover all partial periodic spatial patterns in a spatiotemporal database. This algorithm employs a novel smart depth-first search technique to discover desired patterns effectively. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in the air pollution database.

In real-world databases, high utility itemset (HUI) is an important class of regularities. Most previous studies have focused on mining HUIs in transactional databases and did not consider the spatiotemporal characteristics of items. In this study, a more flexible model of spatial HUIs (SHUIs) that exist in spatiotemporal databases is proposed. In a spatiotemporal database (STD), an itemset is said to be an SHUI if its utility is not less than a user-specified minimum utility and the distance between any two of its items is not more than a user-specified maximum distance. Identifying SHUIs is very challenging because the generated itemsets do not satisfy the anti-monotonic property. In this study, we present two novel pruning techniques for reducing computational costs. Moreover, a fast single scan algorithm is presented for effectively evaluating all SHUIs in a STD. Furthermore, two case studies are presented, in which the proposed model is used to identify useful information in traffic congestion data and air pollution data.

Weighted Frequent Itemset (WFI) mining is an important model in data mining. The popular adoption and successful industrial application of this model has been hindered by the following two obstacles: (i) finding WFIs is a computationally expensiveness process as these itemsets do not satisfy the downward closure property and (ii) lack of parallel algorithms to find WFIs in very large databases (e.g. astronomical data and twitter data). This paper makes an effort to address these two obstacles. Two pattern-growth algorithms, Sequential Weighted Frequent Pattern-growth and Parallel Weighted Frequent Pattern-growth, have been introduced to discover WFIs efficiently. Both algorithms employ three novel pruning techniques to reduce the computational cost effectively. The first pruning technique prunes some of the uninteresting items by employing a criterion known as cutoff weight. The second pruning technique, called conditional pattern base elimination, eliminates the construction of conditional pattern bases if a suffix item is an uninteresting item. The third pruning technique, called pattern-growth termination, defines a new terminating condition for the pattern-growth technique. Experimental results demonstrate that the proposed algorithms are memory and runtime efficient, and highly scalable as well.

A temporal database is a collection of transactions, ordered by their timestamps. Discovering partial periodic itemsets in temporal databases has numerous applications. However, to the best of our knowledge, no work has considered finding these itemsets in temporal databases, despite that this type of data is very common in real-life. Discovering partial periodic itemsets in temporal databases is challenging. It requires defining (i) an appropriate measure to assess the periodic interestingness of itemsets, and (ii) an algorithm to efficiently find all partial periodic itemsets. While a pattern-growth algorithm can be employed for the second sub-task, the first sub-task has not been addressed. Moreover, how these two tasks are combined has significant implications. In this paper, we address this challenge. We introduce a model to find partial periodic itemsets in temporal databases. A new measure, called periodic-frequency, has been proposed to determine the periodic interestingness of itemsets by taking into account their number of cyclic repetitions in the entire data. Moreover, the paper introduces a pattern-growth algorithm to discover all partial periodic itemsets. Experimental results demonstrate that our model is efficient.

The development of cloud storage and computing has facilitated the rise of various big data applications. As a representative high performance computing (HPC) workload, graph processing is becoming a part of cloud computing. However, scalable computing on large graphs is still dominated by HPC solutions, which require high performance all-to-all collective operations over torus (or mesh) networking. Implementing those torus-based algorithms on commodity clusters, e.g., cloud computing infrastructures, can result in great latency due to inefficient communication. Moreover, designing a highly scalable system for large social graphs, is far from being trivial, as intrinsic features of social graphs, e.g., degree skewness and lacking of locality, often profoundly limit the extent of parallelism.

Partial periodic patterns are an important class of regularities that exist in a time series. A key property of these patterns is that they can start, stop, and restart anywhere within a series. We classify partial periodic patterns into two types: (i) regular patterns−patterns exhibiting periodic behavior throughout a series with some exceptions and (ii) recurring patterns−patterns exhibiting periodic behavior only for particular time intervals within a series. Past studies on partial periodic search have been primarily focused on finding regular patterns. One cannot ignore the knowledge pertaining to recurring patterns. This is because they provide useful information pertaining to seasonal or temporal associations between events. Finding recurring patterns is a non-trivial task because of two main reasons. (i) Each recurring pattern is associated with temporal information pertaining to its durations of periodic appearances in a series. Obtaining this information is challenging because the information can vary within and across patterns. (ii) Finding all recurring patterns is a computationally expensive process since they do not satisfy the anti-monotonic property. In this paper, we propose recurring pattern model by addressing the above issues. We also propose Recurring Pattern growth algorithm along with an efficient pruning technique to discover these patterns. Experimental results show that recurring patterns can be useful and that our algorithm is efficient.

Coverage pattern mining is an important model in data mining. It provides useful information pertaining to the sets of items that have coverage interesting to the users in a transactional database. The coverage patterns do not satisfy the anti-monotonic property. This increases the search space in the itemset lattice, which in turn increases the computational cost of mining these patterns. An Apriori-like algorithm known as CMine has been proposed in the literature to discover the patterns. It employs a pruning technique to reduce the search space. We have observed that there exists further scope for reducing the search space effectively. In this paper, we theoretically analyze different measures used in the pattern model, and introduce a novel pruning technique to reduce the search space. An Apriori-like algorithm, called CMine++, has also been proposed to discover the patterns. The performance study shows that mining coverage patterns with CMine++ is efficient.

Frequent patterns are an important class of regularities that exist in a transaction database. Certain frequent patterns with low minimum support (minsup) value can provide useful information in many real-world applications. However, extraction of these frequent patterns with single minsupbased frequent pattern mining algorithms such as Apriori and FP-growth leads to “rare item problem. ” That is, at high minsup value, the frequent patterns with low minsup are missed, and at low minsup value, the number of frequent patterns explodes. In the literature,“multiple minsups framework” was proposed to discover frequent patterns. Furthermore, frequent pattern mining techniques such as Multiple Support Apriori and Conditional Frequent Pattern-growth (CFP-growth) algorithms have been proposed. As the frequent patterns mined with this framework do not satisfy downward closure property, the algorithms follow different types of pruning techniques to reduce the search space. In this paper, we propose an efficient CFP-growth algorithm by proposing new pruning techniques. Experimental results show that the proposed pruning techniques are effective.

In this paper we have proposed an improved approach to extract rare association rules. Rare association rules are the association rules containing rare items. Rare items are less frequent items. For extracting rare itemsets, the single minimum support (minsup) based approaches like Apriori approach suffer from ldquorare item problemrdquo dilemma. At high minsup value, rare itemsets are missed, and at low minsup value, the number of frequent itemsets explodes. To extract rare itemsets, an effort has been made in the literature in which minsup of each item is fixed equal to the percentage of its support. Even though this approach improves the performance over single minsup based approaches, it still suffers from ldquorare item problemrdquo dilemma. If minsup for the item is fixed by setting the percentage value high, the rare itemsets are missed as the minsup for the rare items becomes close to their support, and if minsup for the item is fixed by setting the percentage value low, the number of frequent itemsets explodes. In this paper, we propose an improved approach in which minsup is fixed for each item based on the notion of ldquosupport differencerdquo. The proposed approach assigns appropriate minsup values for frequent as well as rare items based on their item supports and reduces both ldquorule missingrdquo and ldquorule explosionrdquo problems. Experimental results on both synthetic and real world datasets show that the proposed approach improves performance over existing approaches by minimizing the explosion of number of frequent itemsets involving frequent items and without missing the frequent itemsets involving rare items.