Structure identification in relational data

This paper presents several investigations into the prospects for identifying meaningful structures in empirical data, namely, structures permitting effective organization of the data to meet requirements of future queries. We propose a general framework whereby the notion of identifiability is given a precise formal definition similar to that of learnability. Using this framework, we then explore if a tractable procedure exists for deciding whether a given relation is decomposable into a constraint network or a CNF theory with desirable topology and, if the answer is positive, identifying the desired decomposition. Finally, we address the problem of expressing a given relation as a Horn theory and, if this is impossible, finding the best k-Horn approximation to the given relation. We show that both problems can be solved in time polynomial in the length of the data

The Relational Vector-space Model

This paper addresses the classification of linked entities. We introduce a relational vector (VS) model (in analogy to the VS model used in information retrieval) that abstracts the linked structure, representing entities by vectors of weights. Given labeled data as background knowledge training data, classification procedures can be defined for this model, including a straightforward, "direct" model using weighted adjacency vectors. Using a large set of tasks from the domain of company affiliation identification, we demonstrate that such classification procedures can be effective. We then examine the method in more detail, showing that as expected the classification performance correlates with the- relational auto correlation of the data set. We then turn the tables and use the relational VS scores as a way to analyze/visualize the relational autocorrelation present in a complex linked structure. The main contribution of the paper 1s to introduce the relational VS model as a potentially useful addition to the toolkit for relational data mining. It could provide useful constructed features for domains with low to moderate relational autocorrelation; it may be effective by itself for domains with high levels of relational autocorrelation, and it provides a useful abstraction for analyzing the properties of linked data.Information Systems Working Papers Serie

"Natural relations" : a note on X'-structure

With the rise of minimalism, many concepts related to the geometrical relations of phrase structure held fast to in earlier approaches have been reconsidered. This article deals with distinguishing (relational and technical) properties of specifiers and adjuncts in a Bare Phrase Structure framework (X'-Theory). I extend specific aspects of X-structure relevant to the discussion of specifiers vs. adjuncts. I argue that unique specifiers can be derived from the system and that adjunction, possibly multiple, results from Direct Merge only. The final product is a series of relationships in line with recent thoughts and minimalist premises, but formally more similar to earlier conceptions of the X'-schema. I address conceptual, empirical and theoretical arguments against multiple specifiers and related issues next, that is beyond the predictions immediately following from the tripartitional view of clause structure proposed in Grohmann (2000). After laying out my motivations to critically consider the issue, I present a set of data that casts serious doubt over the justifications offered to replace Agr with v as the accusative casemarker. Having conceptual and empirical back-up, I then tackle the theoretical validity of specifiers, and ways to distinguish unique specifiers from (multiple) adjuncts. I introduce a version of Bare Phrase Structure that does so, yet keeps the spirit of defining structural identification over relational rather than categorial properties

Interactive Process Identification and Selection from SAP ERP

SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes that are contained in a specific SAP instance, and the creation of a list of related tables is a significant challenge. Eventually, one needs to extract an event log for process mining purposes from SAP ERP. This demo paper shows the tool Interactive SAP Explorer that tackles the process identification and selection problem by encoding the relational structure of SAP ERP in a labeled property graph. Our approach allows asking complex process-related queries along with advanced representations of the relational structure

PropBase QueryLayer: a single portal to UK physical property databases

Until recently, the delivery of geological information for industry and public was achieved by geological mapping. Now pervasively available computers mean that 3D geological models can deliver realistic representations of the geometric location of geological units, represented as shells or volumes. The next phase of this process is to populate these with physical properties data that describe subsurface heterogeneity and its associated uncertainty. Achieving this requires capture and serving of physical, hydrological and other property information from diverse sources to populate these models. The British Geological Survey (BGS) holds large volumes of subsurface property data, derived both from their own research data collection and also other, often commercially derived data sources. This can be voxelated to incorporate this data into the models to demonstrate property variation within the subsurface geometry. All property data held by BGS has for many years been stored in relational databases to ensure their long-term continuity. However these have, by necessity, complex structures; each database contains positional reference data and model information, and also metadata such as sample identification information and attributes that define the source and processing. Whilst this is critical to assessing these analyses, it also hugely complicates the understanding of variability of the property under assessment and requires multiple queries to study related datasets making extracting physical properties from these databases difficult. Therefore the PropBase Query Layer has been created to allow simplified aggregation and extraction of all related data and its presentation of complex data in simple, mostly denormalized, tables which combine information from multiple databases into a single system. The structure from each relational database is denormalized in a generalised structure, so that each dataset can be viewed together in a common format using a simple interface. Data are re-engineered to facilitate easy loading. The query layer structure comprises tables, procedures, functions, triggers, views and materialised views. The structure contains a main table PRB_DATA which contains all of the data with the following attribution: • a unique identifier • the data source • the unique identifier from the parent database for traceability • the 3D location • the property type • the property value • the units • necessary qualifiers • precision information and an audit trail Data sources, property type and units are constrained by dictionaries, a key component of the structure which defines what properties and inheritance hierarchies are to be coded and also guides the process as to what and how these are extracted from the structure. Data types served by the Query Layer include site investigation derived geotechnical data, hydrogeology datasets, regional geochemistry, geophysical logs as well as lithological and borehole metadata. The size and complexity of the data sets with multiple parent structures requires a technically robust approach to keep the layer synchronised. This is achieved through Oracle procedures written in PL/SQL containing the logic required to carry out the data manipulation (inserts, updates, deletes) to keep the layer synchronised with the underlying databases either as regular scheduled jobs (weekly, monthly etc.) or invoked on demand. The PropBase Query Layer’s implementation has enabled rapid data discovery, visualisation and interpretation of geological data with greater ease, simplifying the parameterisation of 3D model volumes and facilitating the study of intra-unit heterogeneity

Structure motivator: a tool for exploring small three-dimensional elements in proteins

<br>Background: Protein structures incorporate characteristic three-dimensional elements defined by some or all of hydrogen bonding, dihedral angles and amino acid sequence. The software application, Structure Motivator, allows interactive exploration and analysis of such elements, and their resolution into sub-classes.</br> <br>Results: Structure Motivator is a standalone application with an embedded relational database of proteins that, as a starting point, can furnish the user with a palette of unclassified small peptides or a choice of pre-classified structural motifs. Alternatively the application accepts files of data generated externally. After loading, the structural elements are displayed as two-dimensional plots of dihedral angles (φ/ψ, φ/χ1 or in combination) for each residue, with visualization options to allow the conformation or amino acid composition at one residue to be viewed in the context of that at other residues. Interactive selections may then be made and structural subsets saved to file for further sub-classification or external analysis. The application has been applied both to classical motifs, such as the β-turn, and ‘non-motif’ structural elements, such as specific segments of helices.</br> <br>Conclusions: Structure Motivator allows structural biologists, whether or not they possess computational skills, to subject small structural elements in proteins to rapid interactive analysis that would otherwise require complex programming or database queries. Within a broad group of structural motifs, it facilitates the identification and separation of sub-classes with distinct stereochemical properties.</br&gt

Medical ontology for treatment of clinical data from children and youth

The use of information technologies in the field of biomedical data management has grown considerably and is today one of the main fields of use of these technologies. There are several advantages arising either to an individual’s health or to public health, particularly because access to clinical data become available anywhere access via the Internet or individual health card. This card will contain personal data accessible from a terminal card reader, identical to the citizen card. This work focuses on the development of an ontology of universal data structure so that the information is accessible and organized in the same way, regardless of the system that use them. In this context there is the need to incorporate security mechanisms, the respect of ethical principles underlying the management and maintenance of clinical data, ensuring maximum confidentiality. To develop the proposed ontology, for the treatment of clinical data of children and youth is used as reference bulletin health in Portugal. Using this structure, it follows the clear and unambiguous identification of the fields required for registration of clinical information, standardized in a relational model. To ensure the confidentiality of data, identification of the individual is only the number of national health system and are not recorded on the card personal data such as name, address or contact forms

The Role Of Industry Structure On Customer Value In Robotic Surgery

Spending on robot surgery is expected to increase by $17 billion in the next 6 years. This new surgical treatment has challenged hospitals with higher costs and varying performance. Healthcare executives struggle balancing the adoption of medical innovations with managing healthcare costs. This dilemma can be further complicated by industry structures relative to capital-intensive medical innovations. This research explores the interaction between industry structure and customer value. Specifically, how can hospitals apply an understanding of supplier industry structure and customer value to improve the value of a robotic surgery program (RSP)? This industry study represents an exhaustive longitudinal review of over 15 years of public data relative to robotic surgery, across three distinct time periods. Within the research, industry structure is evaluated using Porter’s 5-forces model. A framework based upon contributions from Grönroos as well as Menon, Homburg, and Beutin is introduced to assess customer value based upon clinical, financial and strategic (CFS) value. The implications of periodic industry structure on customer value were examined to identify opportunities for hospital executives to increase RSP customer value. There were several empirical and theoretical findings from this research. First, in the face of increasing industry structure the identification of favorable forces may create opportunities to increase RSP value. Secondarily, exploring customer value through the lens of core, add-on, relational and transactional benefits in the sub-context of CFS value aids in the identification of market power influences on customer value. The implications of the absence of high levels of relational and transactional benefits without high levels of core and add-on benefits may influence avenues of pursuit in improving RSP value overall. The research also suggests that clinical and strategic value was present despite varying degrees of industry structure. Finally, this study represents an empirical joint analysis of industry structure and customer value in robotic surgery. Some proponents may find the introduction of an integrative model for measuring customer value in robotic surgery, applicable to other capital-intensive medical innovations or disruptive technologies at large

A Product Line Systems Engineering Process for Variability Identification and Reduction

Software Product Line Engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a Product Line Systems Engineering process is proposed. Specifically, the process extends research in the System Orthogonal Variability Model to support hierarchical variability modeling with formal definitions; utilizes Systems Engineering concepts and legacy system models to build the hierarchy for the variability model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subject to system model availability, reduction of 14% to 40% in the number of variation points are demonstrated in the case studies.Comment: 12 pages, 6 figures, 2 tables; submitted to the IEEE Systems Journal on 3rd June 201