Time Matters

This chapter introduces a framework and a methodology to manage a SaaS application on top of a PaaS infrastructure. This framework utilizes PaaS policy sets to implement the SaaS provider’s elasticity policy for its application server tier. Adaptation is based on strategy-trees, which allow for systematic capture, representation and reasoning about adaptation variability, based on hierarchically organizing different levels of temporal granularity. Thus, a strategy-tree is utilized at the SaaS layer to actively guide policy set selection at runtime in order to maintain alignment with the SaaS provider’s business objectives. This way, the SaaS provider’s attitudes and preferences reflecting their general business needs are incorporated into the adaptation mechanism in an organized and accessible manner. Results from an experiment conducted on a real cloud are presented in support of this approach.

This study systematically broadens the relevance of possible model performance asymmetries across business cycles in the spirit of the recent state‐dependent forecast evaluation literature (e.g. Chauvet & Potter, 2013) to hundreds of macroeconomic indicators and deepens the forecast evaluation of the recent factor model literature on hundreds of target variables (e.g. Stock & Watson, 2012b) in a state‐dependent manner. Our results are consistent with both strands of the literature and generalize the former to over 200 macroeconomic indicators and differentiate the latter across three levels of temporal granularity: We document systematic model performance differences in both absolute and relative terms across business cycles (longitudinal) as well as across variable groups (cross‐sectional) and find these performance differences to be robust across several alternative specifications. The cross‐sectional prevalence and robustness of state dependency shown in this article encourages economic forecasters to complement model performance assessments with a state‐dependent evaluation of predictive ability.

For companies acting on a global scale, the necessity to monitor and analyze news channels and consumer-generated media on the Web, such as weblogs and n news-groups, is steadily increasing. In particular the identification of novel trends and upcoming issues, as well as their dynamic evolution over time, is of utter importance to corporate communications and market analysts. Automated machine learning systems using clustering techniques have only partially succeeded in addressing these newly arising requirements, failing in their endeavor to properly assign short-term hype topics to long-term trends. We propose an approach which allows to monitor news wire on different levels of temporal granularity, extracting key-phrases that reflect short-term topics as well as longer-term trends by means of statistical language modelling. Moreover, our approach allows for assigning those windows of smaller scope to those of longer intervals.

KNAVE-II is an intelligent interface to a distributed web-based architecture that enables users (e.g., physicians) to query, visualize and explore clinical time-oriented databases. Based on prior studies, we have defined a set of requirements for provision of a service for interactive exploration of time oriented clinical data. The main requirements include the visualization, interactive exploration and explanation of both raw data and multiple levels of concepts abstracted from these data; the exploration of clinical data at different levels of temporal granularity along both absolute (calendar-based) and relative (clinically meaningful) time-lines; the exploration and dynamic visualization of the effects of simulated hypothetical modifications of raw data on the derived concepts; and the provision of generic services (such as statistics, documentation, fast search and retrieval of clinically significant concepts, amongst others). KNAVE-II has been implemented and is currently evaluated by expert clinicians in several medical domains, such as oncology, involving monitoring of chronic patients.

Accurate prediction of citywide crowd activity levels (CALs), i.e., the numbers of participants of citywide crowd activities under different venue categories at certain time and locations, is essential for the city management, the personal service applications, and the entrepreneurs in commercial strategic planning. Existing studies have not thoroughly taken into account the complex spatial and temporal interactions among different categories of CALs and their extreme occurrences, leading to lowered adaptivity and accuracy of their models. To address above concerns, we have proposed IE-CALP , a novel spatio-temporal I nteractive attention-based and E xtreme-aware model for C rowd A ctivity L evel P rediction. The tasks of IE-CALP consist of (a) forecasting the spatial distributions of various CALs at different city regions (spatial CALs), and (b) predicting the number of participants per category of the CALs (categorical CALs). To realize above, we have designed a novel spatial CAL-POI interaction-attentive learning component in IE-CALP to model the spatial interactions across different CAL categories, as well as those among the spatial urban regions and CALs. In addition, IE-CALP incorporate the multi-level trends (e.g., daily and weekly levels of temporal granularity) of CALs through a multi-level temporal feature learning component. Furthermore, to enhance the model adaptivity to extreme CALs (e.g., during extreme urban events or weather conditions), we further take into account the extreme value theory and model the impacts of historical CALs upon the occurrences of extreme CALs. Extensive experiments upon a total of 738,715 CAL records and 246,660 POIs in New York City (NYC), Los Angeles (LA), and Tokyo have further validated the accuracy, adaptivity, and effectiveness of IE-CALP ’s interaction-attentive and extreme-aware CAL predictions.

With the emerging technology of mobile edge computing and the ever-increasing demand for low-latency multimedia services, it is intriguing research to predict online multimedia content popularity at the fine level of geographic granularity for potential mobile and edge services. However, such local popularity prediction poses new challenges, including data sparsity, the correlation between user behaviors and contextual information (e.g., geographical PoI distribution), making traditional regression-based popularity prediction inefficient. In this paper, we present a context-embedded local popularity prediction framework. First, based on measurement studies of 2 million users watching 0.3 million videos using an mobile video app, we reveal the characteristics of “local popularity”: contextual information can significantly improve local popularity prediction; Second, we propose a context-embedded LSTM recurrent network, leveraging the correlation between the geographical context and content popularity to estimate a fine-grained content popularity; Finally, we carry our trace-driven experiments to show that our design can significantly improve the popularity prediction accuracy, by up to \(35\%\) against conventional regression models.

Prediction of the popularity of online textual snippets gained much attention in recent years. In this paper we investigate some of the factors that contribute to popularity of specific phrases such as Twitter hashtags. We define a new prediction task and propose a linguistically motivated algorithm for accurate prediction of hashtag popularity. Our prediction algorithm successfully models the interplay between various constraints such as the length restriction, typing effort and ease of comprehension. Controlling for network structure and social aspects we get a glimpse into the processes that shape the way we produce language and coin new words. In order to learn the interactions between the constraints we cast the problem as a ranking task. We adapt Gradient Boosted Trees for learning ranking functions in order to predict the hashtags/neologisms to be accepted. Our results outperform several baseline algorithms including SVM-rank, while maintaining higher interpretability, thus our model's prediction power can be used for better crafting of future hashtags.

Predicting the security threats on the spreading of rumor, false information of Facebook content based on the principle of sociology

Finite element spaces on a tetrahedron are constructed for div ⁡ div \operatorname {div}\operatorname {div} -conforming symmetric tensors in three dimensions. The key tools of the construction are the decomposition of polynomial tensor spaces and the characterization of the trace operators. First, the div ⁡ div \operatorname {div}\operatorname {div} Hilbert complex and its corresponding polynomial complexes are presented. Several decompositions of polynomial vector and tensor spaces are derived from the polynomial complexes. Second, traces for the div ⁡ div \operatorname {div}\operatorname {div} operator are characterized through a Green’s identity. Besides the normal-normal component, another trace involving combination of first order derivatives of the tensor is continuous across the face. Due to the smoothness of polynomials, the symmetric tensor element is also continuous at vertices, and on the plane orthogonal to each edge. Besides, a finite element for s y m c u r l symcurl -conforming trace-free tensors is constructed following the same approach. Putting all together, a finite element div ⁡ div \operatorname {div}\operatorname {div} complex, as well as the bubble functions complex, in three dimensions is established.

Content popularity evolution impacts optimization of cache placement and therefore influences design of caching policies. In this paper, we propose a cache updating scheme with popularity prediction and energy cost consideration. Firstly, we use linear regression method to predict future content popularity. Furthermore, we update the preceding cache placement according to predicted popularity aiming at minimizing energy cost. The problem is formulated as a convex optimization problem. Then we transform cache updating cost function, such that the optimal cache updating solution can be gained by Karush-Kuhn-Tucker (KKT) conditions. At last, the simulation results show that our scheme gets better performance in terms of energy consumption compared to the scheme without cache updating energy cost consideration.

Social Bookmarking Systems (SBS) have been widely adopted in the last years, and thus they have had a significant impact on the way that online content is accessed, read and rated. Until recently, the decision on what content to display in a publisher’s web pages was made by one or at most few authorities. In contrast, modern SBS-based applications permit their users to submit their preferred content, to comment on and to rate the content of other users and establish social relations with each other. In that way, the vision of the social media is realized, i.e. the online users collectively decide upon the interestingness of the available bookmarked content. This article attempts to provide insights into the dynamics emerging from the process of content rating by the user community. To this end, the article proposes a framework for the study of the statistical properties of an SBS, the evolution of bookmarked content popularity and user activity in time, as well as the impact of online social networks on the content consumption behavior of individuals. The proposed analysis framework is applied to a large dataset collected from digg, a popular social media application.

Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process

Business process modelling is an important part of system design. When designing or redesigning a business process, stakeholders specify, negotiate, and agree on business requirements to be satisfied, including non-functional requirements that concern the quality of the business process. This thesis addresses the question of how to specify and compute the quality of a business process, given the model that stakeholders use. The motivation for this thesis is the increasing importance of the quality of business processes. Knowing the quality of specific business processes enables stakeholders to judge if these processes need improvement. Knowing the quality of the constructs of those processes (viz., events, inputs, activities, and outputs) and the way they are structured enables a more detailed analysis of their shortcomings and provides a basis for the design of improvements. The research challenge of this thesis is grounded in the assumption that: “Organisations need an appropriate means to effectively compute achievement of their goals and objectives by their business processes.” Given this challenge, the main research question on which this thesis focuses is: “Can the quality of a business process be computed quantitatively at different levels of granularity?” The research objective is: “To develop frameworks, factors, and metrics for computing non-functional requirements (quality) of business processes quantitatively at different levels of granularity.” The outcomes of this thesis are: 1) BPIMM, a language-independent business process integrating meta-model, based on the concepts of seven mainstream business process modelling languages: BPMN, EPC, RAD, UML AD, SADT, IDEF0, and IDEF3. 2) BPC-QC (Business Process Concept - Quality Computation), an approach to quality computation at the lowest level of granularity of a business process. The approach consists of: i. BPC-QEF (Business Process Concept - Quality Evaluation Framework), a language-independent generic framework and algorithm to compute the quality of the constructs of a business process: event, input, activity, and output. ii. A set of business process quality dimensions and factors. The following quality dimensions are distinguished: performance, efficiency, reliability, recoverability, permissibility, and availability. Each dimension categorises different quality aspects in terms of factors. A non-exhaustive set of sixteen quantitative factors is provided. iii. Quality metrics for each of the quality factors, to facilitate a quantitative computation of the quality of a specific construct of a business process. 3) BP-QC (Business Process - Quality Computation), an approach to compute the quality at the highest level of granularity of a business process. The approach consists of: i. BP-CQCF (Business Process - Compositional Quality Computation Framework), a language-independent generic framework and algorithm to compute the quality of a business process as a whole, given the quality of its constructs. ii. A set of generic business process modelling patterns to decompose a business process into more succinct parts, namely: sequential, parallel with synchronisation, exclusive, inclusive, simple loop, and complex loop. iii. A set of over one hundred computational formulae. For each combination of modelling pattern and a quality factor, there is a formula to compute the quality. 4) AAV (Approach to Application and Validation), an evaluation plan to evaluate BPIMM, BPC-QC and BP-QC in practice, together with expert stakeholders. The plan consists of the units of measure, a measurement model, and a case study procedure. To evaluate the applicability of the contributions of this thesis to real world business needs, four case studies have been conducted in different environments: a Dutch educational institution, a global financial institution, an international financial service provider, and a Dutch research project on crisis management. Each of these case studies concerns a different, single business process. This thesis shows that: 1) A quality computation approach can be adopted independent of a business process modelling language. 2) Quantitative quality factors can be introduced specifically for the constructs of a business process. 3) Quantitative metrics and computational formulae can be developed for specific quality factors, allowing the computation of different aspects of the quality of a business process quantitatively at different levels of granularity. 4) An evaluation plan can be developed to evaluate the applicability of the contributions of this thesis (viz., BPIMM, BPC-QC, and BP-QC). The contributions of this thesis are designed to be beneficial to the areas of business and management, requirements engineering, software engineering, and business process modelling. In the areas of requirements engineering and software engineering, these contributions are intended to help practitioners to consider non-functional requirements at the earliest stage. In the area of business process modelling, information systems, service computing, and cloud computing, the contributions can be used for quality-driven modelling, design, and redesign. To conclude, knowing the quality value of a business process at different levels of granularity provides a basis for its improvement.

Flexible multiphysics simulation of porous electrodes: Conformal to 3D reconstructed microstructures

This paper presents a new distributed computing framework for Many Task Computing (MTC) applications, based on the Extensible Messaging and Presence Protocol (XMPP). A lightweight, highly available system, named Kestrel, has been developed to explore XMPP-based techniques for improving MTC system tolerance to faults that result from scaling and intermittent computing agent presence. By leveraging technologies used in large instant messaging systems that scale to millions of clients, this MTC system is designed to scale to millions of agents at various levels of granularity: cores, machines, clusters, and even sensors, which makes it a good fit for MTC.Kestrel's architecture is inspired by the distributed design of pilot job frameworks on the grid as well as botnets, with the addition of a commodity instant messaging protocol for communications. Whereas botnet command-and-control systems have frequently used a combination of Internet Relay Chat (IRC), Distributed Hash Table (DHT), and other Peer-to-Peer (P2P) technologies, Kestrel utilizes XMPP for its presence notification capabilities, which allow the system to maintain continuous tracking of machine presence and state in real time. XMPP is also easily extensible with application-specific subprotocols, which can be utilized to transfer machine profile descriptions and job requirements. These sub-protocols can be used to implement distributed matching of jobs to systems, using a mechanism similar to ClassAds in the Condor High Throughput Computing (HTC) system.