20,384 research outputs found
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks
IEEE Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking
Метод прогнозування місцезнаходження рухомих об’єктів
Growing popularity of location based services is leading to an increasing volume of mobility data. Inthis paper we introduce a data mining approach to the problem of predicting the next location of amoving object with a certain level of accuracy. We use apriori algorithm to build a probabilisticmodel of future object location. The experiments have demonstrated that our technique givesreasonably accurate predictionРост уровня распространения сервисов, ориентированных на позиционирование движущихсяобъектов, приводит к быстрому росту объемов мобильных данных. В данной статье авторами предложен подход к решению проблемы прогнозирования последующего местонахождения движущегося объекта с определенным уровнем точности. При этом используется априорныйалгоритм для построения вероятностной модели последующего местонахождения объекта.Эксперименты показали, что предложенный подход обеспечивает приемлемый уровень точности предсказанияЗростання рівня розповсюдження сервісів, орієнтованих на позиціонування рухомих об’єктів,призводить до швидкого зростання обсягів мобільних даних. В даній статті авторами запропоновано підхід до розв’язання проблеми прогнозування наступного місцезнаходження рухомого об’єкта з визначеним рівнем точності. При цьому використовується апріорний алгоритмдля побудови імовірнісної моделі майбутнього місцезнаходження об’єкта. Експерименти продемонстрували, що запропонований підхід забезпечує прийнятний рівень точності передбаченн
Trajectory data mining: A review of methods and applications
The increasing use of location-aware devices has led to an increasing availability of trajectory data. As a result, researchers devoted their efforts to developing analysis methods including different data mining methods for trajectories. However, the research in this direction has so far produced mostly isolated studies and we still lack an integrated view of problems in applications of trajectory mining that were solved, the methods used to solve them, and applications using the obtained solutions. In this paper, we first discuss generic methods of trajectory mining and the relationships between them. Then, we discuss and classify application problems that were solved using trajectory data and relate them to the generic mining methods that were used and real world applications based on them. We classify trajectory-mining application problems under major problem groups based on how they are related. This classification of problems can guide researchers in identifying new application problems. The relationships between the methods together with the association between the application problems and mining methods can help researchers in identifying gaps between methods and inspire them to develop new methods. This paper can also guide analysts in choosing a suitable method for a specific problem. The main contribution of this paper is to provide an integrated view relating applications of mining trajectory data and the methods used
Hierarchical organization of functional connectivity in the mouse brain: a complex network approach
This paper represents a contribution to the study of the brain functional
connectivity from the perspective of complex networks theory. More
specifically, we apply graph theoretical analyses to provide evidence of the
modular structure of the mouse brain and to shed light on its hierarchical
organization. We propose a novel percolation analysis and we apply our approach
to the analysis of a resting-state functional MRI data set from 41 mice. This
approach reveals a robust hierarchical structure of modules persistent across
different subjects. Importantly, we test this approach against a statistical
benchmark (or null model) which constrains only the distributions of empirical
correlations. Our results unambiguously show that the hierarchical character of
the mouse brain modular structure is not trivially encoded into this
lower-order constraint. Finally, we investigate the modular structure of the
mouse brain by computing the Minimal Spanning Forest, a technique that
identifies subnetworks characterized by the strongest internal correlations.
This approach represents a faster alternative to other community detection
methods and provides a means to rank modules on the basis of the strength of
their internal edges.Comment: 11 pages, 9 figure
SIMS: A Hybrid Method for Rapid Conformational Analysis
Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their
structure. Describing the exact details of these conformational changes, however, remains a central challenge for
computational biology due the enormous computational requirements of the problem. This has engendered the
development of a rich variety of useful methods designed to answer specific questions at different levels of spatial,
temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally
demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured
Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both
to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm,
borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise
energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate,
analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the
abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic
conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution
for each. These include the automatic determination of ムムactiveメメ residues for the hinge-based system Cyanovirin-N,
exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose-
Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only
determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields,
demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems
Exploring Decomposition for Solving Pattern Mining Problems
This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.publishedVersio
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