Towards Efficient Sequential Pattern Mining in Temporal Uncertain Databases

Uncertain sequence databases are widely used to model data with inaccurate or imprecise timestamps in many real world applications. In this paper, we use uniform distributions to model uncertain timestamps and adopt possible world semantics to interpret temporal uncertain database. We design an incremental approach to manage temporal uncertainty efficiently, which is integrated into the classic pattern-growth SPM algorithm to mine uncertain sequential patterns. Extensive experiments prove that our algorithm performs well in both efficiency and scalability

Mining Uncertain Sequential Patterns in Iterative MapReduce

This paper proposes a sequential pattern mining (SPM) algorithm in large scale uncertain databases. Uncertain sequence databases are widely used to model inaccurate or imprecise timestamped data in many real applications, where traditional SPM algorithms are inapplicable because of data uncertainty and scalability. In this paper, we develop an efficient approach to manage data uncertainty in SPM and design an iterative MapReduce framework to execute the uncertain SPM algorithm in parallel. We conduct extensive experiments in both synthetic and real uncertain datasets. And the experimental results prove that our algorithm is efficient and scalable

Sequential pattern mining with uncertain data

In recent years, a number of emerging applications, such as sensor monitoring systems, RFID networks and location based services, have led to the proliferation of uncertain data. However, traditional data mining algorithms are usually inapplicable in uncertain data because of its probabilistic nature. Uncertainty has to be carefully handled; otherwise, it might significantly downgrade the quality of underlying data mining applications. Therefore, we extend traditional data mining algorithms into their uncertain versions so that they still can produce accurate results. In particular, we use a motivating example of sequential pattern mining to illustrate how to incorporate uncertain information in the process of data mining. We use possible world semantics to interpret two typical types of uncertainty: the tuple-level existential uncertainty and the attribute-level temporal uncertainty. In an uncertain database, it is probabilistic that a pattern is frequent or not; thus, we define the concept of probabilistic frequent sequential patterns. And various algorithms are designed to mine probabilistic frequent patterns efficiently in uncertain databases. We also implement our algorithms on distributed computing platforms, such as MapReduce and Spark, so that they can be applied in large scale databases. Our work also includes uncertainty computation in supervised machine learning algorithms. We develop an artificial neural network to classify numeric uncertain data; and a Naive Bayesian classifier is designed for classifying categorical uncertain data streams. We also propose a discretization algorithm to pre-process numerical uncertain data, since many classifiers work with categoric data only. And experimental results in both synthetic and real-world uncertain datasets demonstrate that our methods are effective and efficient

Spatial Data Quality in the IoT Era:Management and Exploitation

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications

Efficient Matching of Substrings in Uncertain Sequences

Substring matching is fundamental to data mining methods for se-quential data. It involves checking the existence of a short subse-quence within a longer sequence, ensuring no gaps within a match. Whilst a large amount of existing work has focused on substring matching and mining techniques for certain sequences, there are on-ly a few results for uncertain sequences. Uncertain sequences pro-vide powerful representations for modelling sequence behavioural characteristics in emerging domains, such as bioinformatics, sen-sor streams and trajectory analysis. In this paper, we focus on the core problem of computing substring matching probability in un-certain sequences and propose an efficient dynamic programming algorithm for this task. We demonstrate our approach is both com-petitive theoretically, as well as effective and scalable experimental-ly. Our results contribute towards a foundation for adapting classic sequence mining methods to deal with uncertain data.