Detecting anomalous longitudinal associations through higher order mining

The detection of unusual or anomalous data is an important function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it is often difficult to determine which algorithms might detect anomalies given any random dataset. In this paper we provide a partial solution to this problem by elevating the search for anomalous data in transaction-oriented datasets to an inspection of the rules that can be produced by higher order longitudinal/spatio-temporal association rule mining. In this way we are able to apply algorithms that may provide a view of anomalies that is arguably closer to that sought by information analysts.Sydney, NS

Time Series Forecasting for Energy Consumption

In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied. Specifically, accurately predicting energy consumption in a particular building, country, etc., is an important task for properly managing energy efficiency. Moreover, it can be advantageous to carry this out in a short time frame, taking into account the new consumption paradigm. On the other hand, the time horizon must be considered, which can be short-, medium-, or long-term. For this reason, it is important to develop and implement new intelligent models faster and more accurately. In this way, the application of big data and machine learning techniques have become essential to achieve this goal. This Special Issue sought to contribute to the advancement of energy consumption prediction using artificial intelligence models in an optimal and precise manner.PID2020-112495RB-C21B-TIC-42-UGR20“Next Generation EU” Margaritas Sala

Discovering itemset interactions

Itemsets, which are treated as intermediate results in association mining, have attracted significant research due to the inherent complexity of their generation. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable information. This paper presents a novel tree-based approach to discovering item-set interactions, a task which cannot be undertaken by current association mining techniques

Information fusion from multiple databases using meta-association rules

Nowadays, data volume, distribution, and volatility make it difficult to search global patterns by applying traditional Data Mining techniques. In the case of data in a distributed environment, sometimes a local analysis of each dataset separately is adequate but some other times a global decision is needed by the analysis of the entire data. Association rules discovering methods typically require a single uniform dataset and managing with the entire set of distributed data is not possible due to its size. To address the scenarios in which satisfying this requirement is not practical or even feasible, we propose a new method for fusing information, in the form of rules, extracted from multiple datasets. The proposed model produces meta-association rules, i.e. rules in which the antecedent or the consequent may contain rules as well, for finding joint correlations among trends found individually in each dataset. In this paper, we describe the formulation and the implementation of two alternative frameworks that obtain, respectively, crisp meta-rules and fuzzy meta-rules. We compare our proposal with the information obtained when the datasets are not separated, in order to see the main differences between traditional association rules and meta-association rules. We also compare crisp and fuzzy methods for meta-association rule mining, observing that the fuzzy approach offers several advantages: it is more accurate since it incorporates the strength or validity of the previous information, produces a more manageable set of rules for human inspection, and allows the incorporation of contextual information to the mining process expressed in a more human-friendly format

MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH

Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators. The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted

Dominance-based rough set analysis for understanding the drivers of urban development agreements

The rise of neoliberalism in the context of urban development has en- couraged the cooperation between public and private parties. This co- operation is structured by contracts, generally called Urban Develop- ment Agreements (DAs). Being part of the urban regeneration strate- gies, these projects aim at achieving a durable improvement of an area according to sustainability principles. Thus, within the negotiation be- tween private and public, multiple and conflicting instances have to be faced case by case. Despite the uniqueness of each DA, it is possi- ble to define a set of pertinent characteristics that play a crucial role in determining the fairness and appropriateness of the public-private part- nership. Given this context, the work proposes the Dominance Rough Set Approach (DRSA) for exploring the relationship between condi- tion attributes or criteria and decision with the aim of supporting ne- gotiations on the basis of specific features of the DA under evaluation. Specifically, DRSA has been applied on a sample of DAs recently con- cluded in the Lombardy Region, and tested on the other sample of DAs under the negotiation phase. The analysis has accounted for the char- acteristics referring to the following five contexts: urban, institutional, negotiation, development, and economic. The inferred decision rules provide useful knowledge for supporting complex decision processes such as DAs

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets dagger

In This Paper, A Monitoring System Of Agricultural Production Is Modeled As A Data Fusion System (Data From Local Fairs And Meteorological Data). The Proposal Considers The Particular Information Of Sales In Agricultural Markets For Knowledge Extraction About The Associations Among Them. This Association Knowledge Is Employed To Improve Predictions Of Sales Using A Spatial Prediction Technique, As Shown With Data Collected From Local Markets Of The Andean Region Of Ecuador. The Commercial Activity In These Markets Uses Alternative Marketing Circuits (Cialco). This Market Platform Establishes A Direct Relationship Between Producer And Consumer Prices And Promotes Direct Commercial Interaction Among Family Groups. The Problem Is Presented First As A General Fusion Problem With A Network Of Spatially Distributed Heterogeneous Data Sources, And Is Then Applied To The Prediction Of Products Sales Based On Association Rules Mined In Available Sales Data. First, Transactional Data Is Used As The Base To Extract The Best Association Rules Between Products Sold In Different Local Markets, Knowledge That Allows The System To Gain A Significant Improvement In Prediction Accuracy In The Spatial Region Considered.This work was supported in part by Project MINECO TEC2017-88048-C2–2-R, Salesian Polytechnic University of Quito-Ecuador and by Commercial Coordination Network, Ministry of Agriculture and Livestock, Ecuado

An Integrated Approach for Mining Meta-Rules 1

Abstract: An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of scanning database is only twice. In the first time, a set of 1-frequent itemsets and its projection database are formed at every partition. Then every projected database is scanned to construct a hyper-structure. Through mining the hyper-structure, various rules, for example, global association rules, meta-rules, stable association rules and trend rules etc. can be obtained. Compared with existing algorithms for mining association rule, our approach can mine and obtain more useful rules. Compared with existing algorithms for meta-mining or change mining, our approach has higher efficiency. The experimental results show that our approach is very promising

Practice based competency development: a study of resource geologists and the JORC code system

The mining industry is a major contributor to the Australian economy. The value of mining and exploration shares traded on the Australian Stock Exchange are contingent on the estimates of mineral deposits, which are disclosed publically in accordance with a reporting code maintained by the Australasian Joint Ore Reserves Committee (the JORC Code). Expert resource geologists, known as Competent Persons, provide classified estimates of mineral endowment that underpin these public statements. The JORC Code requirements for qualifying as Competent Persons are membership of an approved professional association and a minimum of five years’ relevant experience. This research set out to address a primarily practical issue: How do the mining industry, mining companies and individuals cooperate to develop resource geologists with sufficient competency to provide expert recommendations for public reporting of mineral resources? A corollary to this is ‘Are the current standards sufficient to identify the competency expectations placed on Competent Persons?’ It is challenging to place the subsequent research in any one discipline as the study draws on multiple theories across multiple domains to facilitate a relevant description of the practicebased competency development. To properly understand the the practice of resource geologists operating in a sub-sector within the JORC Code system, the research needed to explore and consolidate diverse theories such as theories on social structures, workplace learning theories and statistical reasoning education theories. In addition, as a mixed methods study, the research draws on a wide range of tools from qualitative iterative coding and theming techniques to the more rigorous statistical tools of t-tests, paired t-tests, ANOVA and the philosophically different Rasch Analysis method. This study reflects a broad curiosity in diverse concepts and theories that is combined with the researcher’s desire to provide a meaningful practical contribution to the mining industry. The practical outcome of this research is a revised set of criteria to meet Competent Persons status under the JORC Code that is supported by a competency development model. These models are generalised to reflect a revised competency model, based on the dual expectations of practice exposure and reasoning ability, and an associated competency development model, which synthesises contributions of workplace learning experiences. The contributions to the theory include a revised theory of workplace learning networks emerging from the practice context of transient professional workers. These networks are enduring, transient and egocentric and operate beyond organisational confines