Fernando Martínez-Plumed, Lidia Contreras-Ochando, Cèsar Ferri, Peter Flach, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana
We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs.
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Relationship-aware sequential pattern mining is the problem of mining frequent patterns in sequences in which the events of a sequence are mutually related by one or more concepts from some respective hierarchical taxonomies, based on the type of the events. Additionally events themselves are also described with a certain number of taxonomical concepts. We present RaSP an algorithm that is able to mine relationship-aware patterns over such sequences; RaSP follows a two stage approach. In the first stage it mines for frequent type patterns and {\em all} their occurrences within the different se…
Frequent-pattern mining is a common approach to reveal the valuable hidden trends behind data. However, existing frequent-pattern mining algorithms are designed for DRAM, instead of persistent memories (PMs), which can lead to severe performance and energy overhead due to the utterly different characteristics between DRAM and PMs when they are running on PMs. In this paper, we propose an efficient and Wear-leveling-aware Frequent-Pattern Mining scheme, WFPM, to solve this problem. The proposed WFPM is evaluated by a series of experiments based on realistic datasets from diversified application…
Facility location problems aim to identify the best locations to set up new services. Majority of the existing works typically assume that the users are static. However, there exists a wide array of services such as fuel stations, ATMs, food joints, etc., that are widely accessed by mobile users besides the static ones. Such trajectory-aware services should, therefore, factor in the trajectories of its users rather than simply their static locations. In this work, we introduce the problem of optimal placement of facility locations for such trajectory-aware services that minimize the user incon…
Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. General purpose graph mining systems provide a generic runtime to explore subgraph structures of interest with the help of user-defined functions that guide the overall exploration process. However, the state-of-the-art graph mining systems remain largely oblivious to the shape (or pattern) of the subgraphs that they mine. This causes them to: (a) explore unnecessary subgraphs; (b) perform expensive computations on the explored subgraphs; and, (c) hold intermediate partial subgraphs in …
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks
本文系统探索LLMs解决语义感知过程挖掘任务的能力，通过上下文学习和监督微调评估五个任务，发现微调后模型在广泛过程类型和行业中表现优异。
Extended Event Log: Towards a Unified Standard for Process Mining
本文提出一种统一的事件日志标准，以解决流程挖掘中事件数据查找、合并和清洗的挑战，从而无需针对每个项目定制日志即可丰富挖掘结果。
Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders. To this end, existing approaches add noise to the results of queries that extract properties of an event log, such as the frequency distribution of trace variants, for analysis.Noise insertion neglects the semantics of the process, though, and may generate traces not present in the original log. This is problematic. It lowers the utility of the published data and makes noise easily identifiable, as some traces…
Process-Aware Information System (PAIS) are IT systems that manages, supports business processes and generate large event logs from execution of business processes. An event log is represented as a tuple of the form CaseID, TimeStamp, Activity and Actor. Process Mining is an emerging area of research that deals with the study and analysis of business processes based on event logs. Process Mining aims at analyzing event logs and discover business process models, enhance them or check for conformance with an a priori model. The large volume of event logs generated are stored in databases. Relati…
Data mining is an increasingly important technology for extracting useful knowledge hidden in large collections of data. There are, however, negative social perceptions about data mining, among which potential privacy violation and potential discrimination. Automated data collection and data mining techniques such as classification have paved the way to making automated decisions, like loan granting/denial, insurance premium computation. If the training datasets are biased in what regards discriminatory attributes like gender, race, religion, discriminatory decisions may ensue. In the first pa…
Quantum Computing and Data Processing for Frequent Itemset Mining
提出量子频繁项集挖掘框架QFM，通过比特向量量子比特编码、挖掘感知候选叠加和比特并行阈值标记三种机制，在IBM Qiskit和Amazon Braket上实现，理论分析和实验表明优于基线方法。
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space
本文提出一种因果感知模式挖掘方案，通过从普适传感器事件序列中提取频繁因果模式并进行加权模式匹配，在无需用户识别的情况下实现高效群体活动识别，实验表明其识别精度优于现有方案且运行时开销较小。
Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc. These methods are usually performed as preprocessing steps and are responsible for transforming complex information into a numerical feature space. Most papers choose existing encoding methods arbitrarily or employ a strategy based on a specific expert knowledge domain. Moreover, existing methods are employed by using their default hyperparameters without evaluating other options. This practice can lead to several drawbacks, such as subopt…
Process mining aims to provide insights into the actual processes based on event data. These data are often recorded by information systems and are widely available. However, they often contain sensitive private information that should be analyzed responsibly. Therefore, privacy issues in process mining are recently receiving more attention. Privacy preservation techniques obviously need to modify the original data, yet, at the same time, they are supposed to preserve the data utility. Privacy-preserving transformations of the data may lead to incorrect or misleading analysis results. Hence, n…
Background. The Drug Discovery Unit (DDU) of Cancer Research UK (CRUK) is using the software Dotmatics for storage and analysis of scientific data during drug discovery process. Whilst the data include event logs, time stamps, activities, and user information are mostly sitting in the database without fully utilising their potential value. Aims. This dissertation aims at extracting knowledge from event logs data which recorded during drug discovery process, to capture the operational business process of the DDU of Cancer Research UK (CRUK) as it was being executed. It provides the evaluations …
Event data is the basis for all process mining analysis. Most process mining techniques assume their input to be an event log. However, event data is rarely recorded in an event log format, but has to be extracted from raw data. Event log extraction itself is an act of modeling as the analyst has to consciously choose which features of the raw data are used for describing which behavior of which entities. Being aware of these choices and subtle but important differences in concepts such as trace, case, activity, event, table, and log is crucial for mastering advanced process mining analyses. T…
In this paper we have focused a variety of techniques, approaches and different areas of the research which are helpful and marked as the important field of data mining Technologies. As we are aware that many Multinational companies and large organizations are operated in different places of the different countries.Each place of operation may generate large volumes of data. Corporate decision makers require access from all such sources and take strategic decisions.The data warehouse is used in the significant business value by improving the effectiveness of managerial decision-making. In an un…
Process mining aims to gain knowledge of business processes via the discovery of process models from event logs generated by information systems. The insights revealed from process mining heavily rely on the quality of the event logs. Activities extracted from different data sources or the free-text nature within the same system may lead to inconsistent labels. Such inconsistency would then lead to redundancy in activity labels, which refer to labels that have different syntax but share the same behaviours. Redundant activity labels could introduce unnecessary complexities to the event logs. T…
Process Mining on Distributed Data Sources
本文针对分布式数据源带来的挑战，提出过程挖掘从离线集中式事件日志向在线分布式传感器数据转变的研究议程，并倡导基于算法工程的方法论，为构建可扩展、隐私感知且以用户为中心的分布式过程挖掘技术提供路线图。
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