Transforming usage data into a sustainable mobile health solution

Mobile health systems typically target patients with widely varying backgrounds, expertise, and needs. These systems must therefore be able to adapt easily to suit the preferences and needs of individual users in the identified patient community. This paper explains how usage logs in a mobile health system can be analyzed to understand how different patients use the system over a period of time. It defines a system usage model built on key interaction events which are captured and then analyzed to obtain patterns of patient behaviour. Based on the patterns of usage obtained from a live mobile health system, the paper shows how the usage model can be used by the mobile health system to drive its day-to-day interactions with the patient. This type of adaptation can significantly influence the sustainability and diffusion of a mobile health initiative into the target patient market.     

Quad trees a data structure for retrieval on composite keys

Summary The quad tree is a data structure appropriate for storing information to be retrieved on composite keys. We discuss the specific case of two-dimensional retrieval, although the structure is easily generalised to arbitrary dimensions. Algorithms are given both for staightforward insertion and for a type of balanced insertion into quad trees. Empirical analyses show that the average time for insertion is logarithmic with the tree size. An algorithm for retrieval within regions is presented along with data from empirical studies which imply that searching is reasonably efficient. We define an optimized tree and present an algorithm to accomplish optimization in n log n time. Searching is guaranteed to be fast in optimized trees. Remaining problems include those of deletion from quad trees and merging of quad trees, which seem to be inherently difficult operations.     

Covering sharing trees: a compact data structure for parameterized verification

The control state reachability problem is decidable for well-structured infinite-state systems like (Lossy) Petri Nets, Vector Addition Systems, and broadcast protocols. An abstract algorithm that solves the problem is the backward reachability algorithm of [1, 21 ]. The algorithm computes the closure of the predecessor operator with respect to a given upward-closed set of target states. When applied to this class of verification problems, symbolic model checkers based on constraints like [7, 26 ] suffer from the state explosion problem.In order to tackle this problem, in [13] we introduced a new data structure, called covering sharing trees, to represent in a compact way collections of infinite sets of system configurations. In this paper, we will study the theoretical complexity of the operations over covering sharing trees needed in symbolic model checking. We will also discuss several optimizations that can be used when dealing with Petri Nets. Among them, in [14] we introduced a new heuristic rule based on structural properties of Petri Nets that can be used to efficiently prune the search during symbolic backward exploration. The combination of these techniques allowed us to turn the abstract algorithm of [1, 21 ] into a practical method. We have evaluated the method on several finite-state and infinite-state examples taken from the literature [2, 18 , 20 , 30 ]. In this paper, we will compare the results we obtained in our experiments with those obtained using other finite and infinite-state verification tools.     

Transforming e-services evaluation data into business analytics using value models

The rapidly increasing penetration and use of the Internet, in conjunction with the explosion of various technologies based on it, gave rise to the development of numerous e-services, such as e-business, e-banking, e-government and e-learning ones. The websites providing these e-services collect large amounts of users’ activity and evaluation data. It is necessary to transform these data into useful business analytics that allow a better understanding of the strengths and weaknesses of the e-service, the various types of value it generates, and its whole value generation mechanism, and at the same time provide guidance for its improvement and optimization. We propose and validate a methodology for transforming user evaluation data into useful business analytics, founded on the technology acceptance model, the IS success model and e-services. We define a three-layer value model for e-services, including concerning its efficiency, effectiveness and impact on users’ future behavior respectively. This value model is used for collecting and processing service evaluation data from users. We calculated two classes of business analytics: average users’ ratings to identify strengths and weaknesses of e-services; and the impacts of first- layer value measures on the second and third layer value ones. The latter allows a better understanding of the value generation mechanism for e-services and identification of improvement priorities. Our methodology has been applied and validated for an e-learning service provided by a university to technology professionals.     

Transforming GIS data into functional road models for large-scale traffic simulation

There exists a vast amount of geographic information system (GIS) data that model road networks around the world as polylines with attributes. In this form, the data are insufficient for applications such as simulation and 3D visualization-tools which will grow in power and demand as sensor data become more pervasive and as governments try to optimize their existing physical infrastructure. In this paper, we propose an efficient method for enhancing a road map from a GIS database to create a geometrically and topologically consistent 3D model to be used in real-time traffic simulation, interactive visualization of virtual worlds, and autonomous vehicle navigation. The resulting representation provides important road features for traffic simulations, including ramps, highways, overpasses, legal merge zones, and intersections with arbitrary states, and it is independent of the simulation methodologies. We test the 3D models of road networks generated by our algorithm on real-time traffic simulation using both macroscopic and microscopic techniques.     

Pattern conserving data structure and algorithms for computations on dendritic trees

The linked list is offered as a pattern conserving data structure, useful for storing neuronal dendritic trees. A BASIC language algorithm is described. Modifications of this algorithm for building linked lists, graphing dendritic trees, and tarry ordering trees are presented. Brief mention is made of the last in first out stack as a alternative data structure for computations on dendritic trees.     

Enhanced layered segment trees: a pragmatic data structure for real-time processing of geometric objects

The data structure that is probably most used in the pattern recognition and image processing of geometric objects is the segment tree and its optimized variant, the “layered segment tree”. In all the versions currently known, except the work of Vaishnavi and Wood described later, these data structures do not operate in real time. Even in the latter scheme, although the structure can be implemented in real time and in an on-line fashion, the operation of insertion involves the sorting of the representations of the line segments in the tree. In essence, for all the reported algorithms, there is no known strategy to insert the segments one by one, other than the trivial strategy of processing them all together as in a batched-mode. In this paper, we present a strategy in which all the operations done on the tree can be done efficiently. Indeed, by improving the bottleneck, we prove that an arbitrary horizontal segment can be inserted into this data structure without invoking an expensive sorting process. We show that while this is accomplished by maintaining the same space and query complexity of the best-known algorithm, the version presented here is applicable to on-line real-time processing of line segments. The paper thus has applications in all areas of pattern recognition and image processing involving geometric objects.     

RE-EM trees: a data mining approach for longitudinal and clustered data

Longitudinal data refer to the situation where repeated observations are available for each sampled object. Clustered data, where observations are nested in a hierarchical structure within objects (without time necessarily being involved) represent a similar type of situation. Methodologies that take this structure into account allow for the possibilities of systematic differences between objects that are not related to attributes and autocorrelation within objects across time periods. A standard methodology in the statistics literature for this type of data is the mixed effects model, where these differences between objects are represented by so-called “random effects” that are estimated from the data (population-level relationships are termed “fixed effects,” together resulting in a mixed effects model). This paper presents a methodology that combines the structure of mixed effects models for longitudinal and clustered data with the flexibility of tree-based estimation methods. We apply the resulting estimation method, called the RE-EM tree, to pricing in online transactions, showing that the RE-EM tree is less sensitive to parametric assumptions and provides improved predictive power compared to linear models with random effects and regression trees without random effects. We also apply it to a smaller data set examining accident fatalities, and show that the RE-EM tree strongly outperforms a tree without random effects while performing comparably to a linear model with random effects. We also perform extensive simulation experiments to show that the estimator improves predictive performance relative to regression trees without random effects and is comparable or superior to using linear models with random effects in more general situations.     

Alan Kay: Transforming the Computer into a Communication Medium

Alan Kay is referred to as the "father of the personal computer" because his 1969 doctoral thesis described an early prototype of personal computing. Kay's ideas contributed to the transformation of the computer from a calculating machine to a communication medium. This article focuses on Kay's vision for personal computing.     

Mapping external views to a common data model

In the context of a multilevel database management system architecture, the problem arises of translating, or mapping, operations at the user's representation level (the External Schema) into operations at the system's common logical level (the Conceptual Schema).In order to support different structured user's data models, the need for an unstructured, view-independent and yet carefully defined data model at the Conceptual Schema level is recognized.In this paper a binary model for the Conceptual Schema is illustrated through the specification of a set of primitives, and the elements of a language for the definition of a binary Schema and of the corresponding operations are given.Procedures are then illustrated for translating external into conceptual operations through exploitation of the primitives of the binary model. Two types of mapping specifications are illustrated: the operational mapping, in which the translation is explicitly defined, and the structural mapping, in which only the structural correspondences between elements of the External and the Conceptual Schema are defined. The automatic mapping between n-ary relational views and the binary Conceptual Schema is finally discussed.     

Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data

The objective of this study was to identify candidate features derived from airborne laser scanner (ALS) data suitable to discriminate between coniferous and deciduous tree species. Both features related to structure and intensity were considered. The study was conducted on 197 Norway spruce and 180 birch trees (leaves on conditions) in a boreal forest reserve in Norway. The ALS sensor used was capable of recording multiple echoes. The point density was 6.6 m^− 2. Laser echoes located within the vertical projection of the tree crowns, which were assumed to be circular and defined according to field measurements, were attributed to three categories: “first echoes of many”, “single echoes”, or “last echoes of many echoes”. They were denoted FIRST, SINGLE, and LAST, respectively. In tree species classification using ALS data features should be independent of tree heights. We found that many features were dependent on tree height and that this dependency influenced selection of candidate features. When we accounted for this dependency, it was revealed that FIRST and SINGLE echoes were located higher and LAST echoes lower in the birch crowns than in spruce crowns. The intensity features of the FIRST echoes differed more between species than corresponding features of the other echo categories. For the FIRST echoes the intensity values tended to be higher for birch than spruce. When using the various features for species classification, maximum overall classification accuracies of 77% and 73% were obtained for structural and intensity features, respectively. Combining candidate features related to structure and intensity resulted in an overall classification accuracy of 88%.     

A counter-example to a lemma about embedding starlike trees into hypercube

In this note, we give a counter-example for a lemma of Harary and Lewinter about partitioning the hypercube with an arbitrary number of vertex-disjoint paths of even length.     

Clustering noisy data in a reduced dimension space via multivariate regression trees

Cluster analysis is sensitive to noise variables intrinsically contained within high dimensional data sets. As the size of data sets increases, clustering techniques robust to noise variables must be identified. This investigation gauges the capabilities of recent clustering algorithms applied to two real data sets increasingly perturbed by superfluous noise variables. The recent techniques include mixture models of factor analysers and auto-associative multivariate regression trees. Statistical techniques are integrated to create two approaches useful for clustering noisy data: multivariate regression trees with principal component scores and multivariate regression trees with factor scores. The tree techniques generate the superior clustering results.     

G-tree: a new data structure for organizing multidimensional data

The author describes an efficient data structure called the G-tree (or grid tree) for organizing multidimensional data. The data structure combines the features of grids and B-trees in a novel manner. It also exploits an ordering property that numbers the partitions in such a way that partitions that are spatially close to one another in a multidimensional space are also close in terms of their partition numbers. This structure adapts well to dynamic data spaces with a high frequency of insertions and deletions, and to nonuniform distributions of data. We demonstrate that it is possible to perform insertion, retrieval, and deletion operations, and to run various range queries efficiently using this structure. A comparison with the BD tree, zkdb tree and the KDB tree is carried out, and the advantages of the G-tree over the other structures are discussed. The simulated bucket utilization rates for the G-tree are also reported     

Transforming an enterprise model into a use case model in business process systems

One of the responsibilities of requirements engineering is to transform stakeholder requirements into system and software requirements. For enterprise systems, this transformation must consider the enterprise context where the system will be deployed. Although there are some approaches for detailing stakeholder requirements, some of them even considering the enterprise context, this task is executed manually. Based on model-driven engineering concepts, this study proposes a semi-automatic transformation from an enterprise model to a use case model. The enterprise model is used as a source of information about the stakeholder requirements and domain knowledge, while the use case model is used as software requirements model. This study presents the source and target metamodels, a set of transformation rules, and a tool to support the transformation. An experiment analyzes the use of the proposed transformation to investigate its benefits and if it can be used in practice, from the point of view of students in the context of a requirements refinement. The results indicate that the approach can be used in practice, as it did not influence the quality of the generated use cases. However, the empirical analysis does not indicate benefits of using the transformation, even if the qualitative results were positive.     

Course management using a data base structure

Elementary algebra is taught at the University of Tennessee at Chattanooga in a computer managed self-paced course. This paper describes the computer management system, including the hardware, the data base, the principal management program and some report generation using an inquiry language.     

Topological entropy and data rate for practical stability: a scalar case

A special class of nonlinear systems, i.e. expanding piecewise affine discrete‐time scalar systems with limited data rate, is used to investigate the role of topological entropy and date rate in a practical stability problem. This special class of nonlinear system as an abstract model is an extension of discrete‐time unstable scalar systems, a well‐known model for quantized feedback design. For such systems with finite quantization levels, how to design a quantized feedback controller to achieve practical stability is considered as a boundability problem. Unlike the existing results about topological entropy for nonlinear stabilization and optimal control for linear systems, for the boundability problem under consideration, the feedback topological entropy defined in this paper is not equal to the minimum number of the quantization interval (i.e. the minimal information rate) and only provides a necessary condition for the boundability of our system under some condition. A necessary and sufficient condition for the boundability is also presented in terms of an inequality related to data rate and the minimum number of quantization intervals. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society