A logic approach for exceptions and anomalies in association rules

Association rules have been used for obtaining information hidden in a database. Recent researches have pointed out that simple associations are insu cient for representing the diverse kinds of knowledge collected in a database. The use of exceptions and anomalies deal with a di erent type of knowledge sometimes more useful than simple associations. Moreover ex- ceptions and anomalies provide a more comprehensive understanding of the information provided by a database. This work intends to go deeper in the logic model studied in [5]. In the model, association rules can be viewed as general relations between two or more attributes quanti ed by means of a convenient quanti er. Using this formulation we establish the true semantics of the distinct kinds of knowledge we can nd in the database hidden in the four folds of the contingency table. The model is also useful for providing some measures for assessing the validity of those kinds of rulesPeer Reviewe

Network Data Mining: Methods and techniques for discovering deep linkage between attributes

Abstract. Network Data Mining identifies emergent networks between myriads of individual data items and utilises special algorithms that aid visualisation of ‘emergent ’ patterns and trends in the linkage. It complements conventional data mining methods, which assume the independence between the attributes and the independence between the values of these attributes. These techniques typically flag, alert or alarm instances or events that could represent anomalous behaviour or irregularities because of a match with pre-defined patterns or rules. They serve as ‘exception detection ’ methods where the rules or definitions of what might constitute an exception are able to be known and specified ahead of time. Many problems are suited to this approach. Many problems however, especially those of a more complex nature, are not well suited. The rules or definitions simply cannot be specified. For example, in the analysis of transaction data there are no known suspicious transactions. This chapter presents a human-centred network data mining methodology that addresses the issues of depicting implicit relationships between data attributes and/or specific values of these attributes. A case study from the area of security illustrates the application of the methodology and corresponding data mining techniques. The chapter argues that for many problems, a ‘discovery’ phase in the investigative process based on visualisation and human cognition is a logical precedent to, and complement of, more automated ‘exception detection ’ phases

A log mining approach for process monitoring in SCADA

SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes. We propose a methodology to systematically identify potential process-related threats in SCADA. Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of log processing. We conduct experiments on a real-life water treatment facility. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular process workflow

An LTL Semantics of Business Workflows with Recovery

We describe a business workflow case study with abnormal behavior management (i.e. recovery) and demonstrate how temporal logics and model checking can provide a methodology to iteratively revise the design and obtain a correct-by construction system. To do so we define a formal semantics by giving a compilation of generic workflow patterns into LTL and we use the bound model checker Zot to prove specific properties and requirements validity. The working assumption is that such a lightweight approach would easily fit into processes that are already in place without the need for a radical change of procedures, tools and people's attitudes. The complexity of formalisms and invasiveness of methods have been demonstrated to be one of the major drawback and obstacle for deployment of formal engineering techniques into mundane projects

Twitter data analysis by means of Strong Flipping Generalized Itemsets

Twitter data has recently been considered to perform a large variety of advanced analysis. Analysis ofTwitter data imposes new challenges because the data distribution is intrinsically sparse, due to a large number of messages post every day by using a wide vocabulary. Aimed at addressing this issue, generalized itemsets - sets of items at different abstraction levels - can be effectively mined and used todiscover interesting multiple-level correlations among data supplied with taxonomies. Each generalizeditemset is characterized by a correlation type (positive, negative, or null) according to the strength of thecorrelation among its items.This paper presents a novel data mining approach to supporting different and interesting targetedanalysis - topic trend analysis, context-aware service profiling - by analyzing Twitter posts. We aim atdiscovering contrasting situations by means of generalized itemsets. Specifically, we focus on comparingitemsets discovered at different abstraction levels and we select large subsets of specific (descendant)itemsets that show correlation type changes with respect to their common ancestor. To this aim, a novelkind of pattern, namely the Strong Flipping Generalized Itemset (SFGI), is extracted from Twitter mes-sages and contextual information supplied with taxonomy hierarchies. Each SFGI consists of a frequentgeneralized itemset X and the set of its descendants showing a correlation type change with respect to X. Experiments performed on both real and synthetic datasets demonstrate the effectiveness of the pro-posed approach in discovering interesting and hidden knowledge from Twitter dat

Searching for Particle Physics Beyond the Standard Model at the LHC and Elsewhere

Following a general introduction to open questions beyond the Standard Model, the prospects for addressing them in the new era opened up by the LHC are reviewed. Sample highlights are given of ways in which the LHC is already probing beyond previous experiments, including the searches for supersymmetry, quark and gluon substructure and microscopic black holes.Comment: 20 pages, 11 figures, talk presented at the 11th conference on "Frontiers of Fundamental Physics", Paris, July 201

Stolen Artwork: Deciding Ownership Is No Pretty Picture

This thesis analyzes the power losses in induction machines and how the losses depend on the harmonic content of the applied voltages. Two cases are compared, one case where a machine is fed with a sinusoidial voltage and one case with a modular multilevel converter (M2C). The sine is representing an ideal grid while the M2C represents a case with harmonic content. The usage of converters for electrical drive systems is increasing due to advantages when the rotor speed could be variable by changing the frequency of the voltage. This is usually increasing the efficiency of the overall system, but is also adding harmonics fed to the machine and switching losses in the converter. Low switching losses in the inverter usually create higher harmonic content that instead increases the losses of the machine. The M2C is then proposed as a converter topology that can keep the harmonic content low while keeping the switching losses relatively low. This study focuses on the iron losses, the part of the total losses that is most hard to predict or measure. Today’s methods used to calculate the iron losses are often rough approximations that do not take the impact of the harmonic content of voltage into consideration, even though the iron losses are dependent on the harmonics. Experimental results in the study show that the losses of a M2C-fed case do not differ much from a sine-fed case. The difference could be explained by low increase of iron losses caused by the small harmonic content from the M2C. The increase of iron losses was linked to the harmonic content of the voltage.Detta examensarbete analyserar effektförluster i induktionsmaskiner och hur förlusterna beror på övertonsinnehållet i den matande spänningen. Två fall kommer att jämföras, ett fall där en maskin är matad från en sinus spänning och ett fall med en modulär multinivå omvandlare (M2C). Sinusen representerar ett idealt nät medan M2C representerar ett fall med övertonsinnehåll. Användning av omvandlare för elekriska drivsystem ökar på grund av fördelarna när rotorhastighet kan varieras genom att ändra frekvensen från den matande växelriktaren. Detta ökar vanligtvis verkningsgraden på det sammanlagda systemet, men detta bidrar även med övertonsinnehåll matat till maskinen och switchförluster i omvandlaren. Låga switchförluster i omvandlaren medför oftast ett högt övertonsinnehåll som istället ökar förlusterna i maskinen. M2C är därför föreslaget som en teknik som håller övertonsinnehållet lågt medan switchförlusterna är relativt låga. Denna studie fokuserar på järnförluster, den del av de totala förlusterna som är som svårast att förutse eller mäta. De metoder som finns för att beräkna järnförlusterna är vanligtvis grova skattningar som inte tar hänsyn till inverkan från spänningens övertoninnehåll, även om järnförluster beror på övertonerna i stor utsträckning. Experimentella resultat i studien visar att förlusterna i ett M2C-matat fall inte avviker i stor utsträkning jämte ett sinusmatat fall. Skillnanen kan förklaras utifrån den lilla ökningen av järnförluster från det låga övertonsinnehållet från M2C:n. Järnförlusterna ses vara kopplade till övertonsinnehållet i spänningen