Visualizing and Interacting with Concept Hierarchies

Concept Hierarchies and Formal Concept Analysis are theoretically well grounded and largely experimented methods. They rely on line diagrams called Galois lattices for visualizing and analysing object-attribute sets. Galois lattices are visually seducing and conceptually rich for experts. However they present important drawbacks due to their concept oriented overall structure: analysing what they show is difficult for non experts, navigation is cumbersome, interaction is poor, and scalability is a deep bottleneck for visual interpretation even for experts. In this paper we introduce semantic probes as a means to overcome many of these problems and extend usability and application possibilities of traditional FCA visualization methods. Semantic probes are visual user centred objects which extract and organize reduced Galois sub-hierarchies. They are simpler, clearer, and they provide a better navigation support through a rich set of interaction possibilities. Since probe driven sub-hierarchies are limited to users focus, scalability is under control and interpretation is facilitated. After some successful experiments, several applications are being developed with the remaining problem of finding a compromise between simplicity and conceptual expressivity

Visual analytics in FCA-based clustering

Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and is a basis for certain types of recommender systems. The problem of triclustering algorithms is that they do not always produce meaningful clusters. This article describes a specific triclustering algorithm and a prototype of a visual analytics platform for working with obtained clusters. This tool is designed as a testing frameworkis and is intended to help an analyst to grasp the results of triclustering and recommender algorithms, and to make decisions on meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary Proceedings of the 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201

An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the Web.Comment: Accepted to appear in IEEE Transactions on Services Computing 201

The development of a scale of the Guttman Type for the assessment of mobility disability in multiple sclerosis

Objective: The aim of the study was to develop a valid and reliable unidimensional scale of the Guttman type for the assessment of mobility disability in multiple sclerosis (MS). Subjects: Sixty-eight subjects with a definite diagnosis of MS participated.They were attending as outpatients at a MS unit at a District General Hospital. Thirty had the primary progressive pattern of disease, and 38 had the relapsing-remitting pattern. Methods: Formal assessments used for neurological disability were inspected, and 14 test items of gross motor function were extracted and ordered according to two criteria. These were that actions progressed from lying, to sitting, to standing and walking tasks, and that they progressed from broader to narrower bases of support. All subjects carried out all test items which were scored as ‘pass’ or ‘fail’. Analysis: Data were tested for internal consistency, reliability, inter item correlation, reproducibility and scalability. On the basis of the results, the items were re-ordered in rank, and reduced to eleven tests. The eleven item scale was re-analysed. Results: Results showed that the scale had an internal consistency of 0.88 (alpha coefficient) and a coefficient of reproducibility (CR) of 0.95 and above for both MS subject groups. The coefficient of scalability (CS) for items was 0.78 for primary progressive subjects and 0.74 for the relapsing-remitting group. Reliability ranged from good (kappa = 0.49) for one item, to perfect for six items. Conclusion: The scale was demonstrated to be a hierarchical scale of the Guttman type exhibiting homogeneous unidimensionality and good reliability. The high CR indicated that scores may be summed, and the very acceptable levels of CS indicated that the cumulative scores are meaningful within the defined concept of hierarchy used in this study

Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework

While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganter's classic algorithm by introducing a family of MR* algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithm's lineage. To evaluate the factors that impact distributed algorithm performance, we compare our MR* algorithms with the state-of-the-art. Experiments conducted on real datasets demonstrate that MRGanter+ is efficient, scalable and an appealing algorithm for distributed problems.Comment: 17 pages, ICFCA 201, Formal Concept Analysis 201