A Multi-Granularity Relation Graph Aggregation Framework with Multimodal Clues for Social Relation Reasoning

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

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.

In traditional formal concept analysis, the attributes in the formal context are considered fixed. However, in the real world data set, attributes always have different levels of granularity, correspondingly, the derived concept lattice may reveal different information and patterns. Therefore, the capability to change the level of granularity of an attribute in formal concept analysis to capture relevant patterns in data is a natural requirement. In this paper, a theoretical study has been undertaken in multi-scale formal contexts, where attributes with different levels of granularity possess different attribute values. Two types of formal concepts, i.e., object-oriented and property-oriented multi-scale concepts, are introduced and studied in detail. The collection of object-oriented concept lattices and property-oriented concept lattices can be obtained at different granularity levels of attributes. It has been shown that the set of extents in the derived concept lattices increases when we choose to use a finer level of granularity. Moreover, a corresponding bidirectional approach to concept construction(i.e., from coarser to finer and from finer to coarser, respectively) is exhibited, and some characterization theorems have been obtained.

In this paper we present a system for automatic Arabic text diacritization using three levels of analysis granularity in a layered back off manner. We build and exploit diacritized language models (LM) for each of three different levels of granularity: surface form, morphologically segmented into prefix/stem/suffix, and character level. For each of the passes, we use Viterbi search to pick the most probable diacritization per word in the input. We start with the surface form LM, followed by the morphological level, then finally we leverage the character level LM. Our system outperforms all of the published systems evaluated against the same training and test data. It achieves a 10.87% WER for complete full diacritization including lexical and syntactic diacritization, and 3.0% WER for lexical diacritization, ignoring syntactic diacritization.

Data warehousing is widely used in industry for reporting and analysis of huge volumes of data at different levels of detail. In general, data warehouses use standard dimensional schema designs to organize their data. However, current data warehousing schema designs fall short in their ability to model the multi-granular data found in various real-world application domains. For example, modern farm equipment in a field produces massive amounts of data at different levels of granularity that has to be stored and queried. A study of the commonly used data warehousing schemas exposes the limitation that the schema designs are intended to simply store data at the same single level of granularity. This paper on the other hand, presents several extended dimensional data warehousing schema design alternatives to store both detail and aggregated data at different levels of granularity. The paper presents three solutions to design the time dimension tables and four solutions to design the fact tables. Moreover, each of these solutions is evaluated in different combinations of the time dimension and the fact tables based on a real world farming case study.KeywordsMulti-granular datadata warehousingdata warehousing schema design

Geo‐SOMis a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in differentGeo‐SOMsizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combinesGeo‐SOMand hierarchical clustering to tackle this problem.Geo‐SOMwas used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the method's effectiveness. As a case study, the rush hour motorcycle flow data inTaipeiCity,Taiwan were analyzed. Compared with the best result generated solely byGeo‐SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the originalGeo‐SOMonly identified two. Among the four hierarchical clustering methods,Ward's clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data.

Due to the rapid development of social media networks, as well as the recent advances on smartphone technologies, users stopped being only consumers and became data producers. It enabled a new form of sensing, engaging a variety of studies using Location-Based Social Networks (LBSN) data. For instance, in device-to-device(D2D) communication systems, the user location is required to make mobile data offloading possible. However, getting the right location is a challenging task, and the analyses of LBSN datasets provide large-scale studies at a low-cost. On the other hand, depending the the level of granularity used on the analyses, it may result in the success or failure of the prediction. Therefore, in this paper, we show the importance of combining the different levels of granularities for studying user behavior in order to obtain a robuster analysis.

In this retrospective qualitative study, the researcher reviewed 55 client records of The Breath Odour Clinic. The purpose was to determine if individuals attended a clinic specialised in treating oral malodour for medical or social reasons. The study focused on the psychosocial and breath odour history. Clients had agreed to the use of information for research purposes. Society uses odour as a means to define and interact with the world. The olfactory, smelling experience is intimate, emotionally charged and connects us with the world. It follows that the smell from mouth breath odour can connect or disconnect a person from their social environment and intimate relationships. How one experiences one's own body is very personal and private but also very public. Breath odour is public as it occurs within a social and cultural context and personal as it affects one's body image and self-confidence. Body image, self-image and social relations mesh, interact and impact upon each other. Breath odour is a dynamic and interactive aspect of the self-image. In addition, breath odour may be value-coded as 'bad'. In 75% of the cases reviewed, decreased self-confidence and insecurity in social and intimate relations led clients to seek treatment at the specialised breath odour clinic. Their doctor, dental hygienist or dentist had treated medical and oral conditions but not resolved their breath odour problem. When a person perceives a constant bad breath problem, she/he uses defence techniques, and may avoid social situations and social relations. This affects a person's well-being.

This paper introduces the concept of granularity into routine dynamics studies. To establish levels of granularity we develop a phenomenological perspective on action, the central category of routine dynamics studies. By conducting an extensive literature review we show that routine dynamics scholars have at least unconsciously applied diverging levels of granularity in their empirical studies. We argue that side-stepping questions of explicitly addressing granularity significantly impedes the comparability of studies since different levels of granularity bring different aspects of performing and patterning processes into focus. Following this, we develop a methodological framework that allows scholars to establish and report on the level of granularity in their study. Finally, we sketch out some implications of taking granularity seriously for writing up research on routine dynamics.

Conformance checking provides a set of techniques for comparing observed behavior (event logs) with process models. Health care providers apply it for verifying compliance between activities realized by the patients and clinical protocols. However, health care processes are characterized by high complexity and variability, and clinical protocols can have a level of granularity different from the one in the recorded events. We proposed a new method for configuring the cost function for the alignment-based conformance checking, handling the different levels of granularity to address this problem. The costs are configured based on grouping activities and the specification of undesirable events. We applied this method using the ProM Framework in a real-world dataset containing events from patients who realized a bariatric surgery in 2016. The technique can also be applied in other conformance checking scenarios, where the granularity of the events differs from the process model. The case study result allowed us to automatically identify critical violations of the clinical protocol by analyzing the fitness metric. The proposed method could help auditors to identify crucial cases.

In order to help Agent or semantic annotation tool in real-time understanding of the text, this paper presents an ontology module partition method: partitioning large broad-coverage ontologism into small ontology modules and storing the partition in advance, in the stage of real-time, according to the application requirements, selecting the appropriate modular from the ontology modular library, then applying directly or composing the selected modules. This time-consuming procedure of ontology extraction and decomposition is put into pre-processing process, which can improve real-time process and improve the efficiency. In this paper, ontology module partition criteria and methods are discussed. The method discussed in this article can partition corresponding ontology in different level of granularities. The ontology relation graph-based ontology module partition algorithm employed extended adjacency list as the storage structure in which the asymptotic time complexity of extended broad first traverse of ontology relation graph is O(n+e).

Understanding social relationships is critical to succeeding in primate societies. In species with complex social networks (including humans), correctly predicting the strength of one's social relationships or bonds helps individuals better navigate future interactions. Social contingency-behaviour that depends on and directly responds to another's actions-is a key feature of interaction that provides opportunities to learn about these relationships. We propose that playful teasing in great apes represents a specialized form of social contingency that enables relationship assessment in a relatively safe context. Playful teasing involves one individual pestering, harassing or provoking another in a playful manner. An ape can learn about their bond by observing how a social partner responds to mild provocation-seeing how far they can push the other before receiving an explicitly negative response. Since responses range from mild aversion to benign tolerance to reciprocal play, the teaser can gain valuable information about relationship quality through monitoring the socially contingent responses to teasing actions. This form of learning through contingency may have evolved as a relatively low-risk method to assess social relationships through direct feedback, though several alternative explanations exist. We examine multiple evolutionary hypotheses for playful teasing and offer suggestions for future empirical testing. This article is part of the theme issue 'Mechanisms of learning from social interaction'.

In this paper we study the effect of granularity on Characteristic Scores and Scales (CSS). Unlike the traditional indicators that are mostly based on means and quantiles, CSS require the reduction of the citation distributions collaboration of the underlying reference population to four states (classes) and thus higher a different level of granularity. While the question of the choice of granularity is at higher levels of aggregation usually not critical since countries and university have rather multidisciplinary profiles, at lower aggregation levels specialisation becomes more typical. Inappropriate granularity might not warrant the depiction of the publication profiles at these levels in a correct and adequate manner and thus not add accurate citation profiles either. In order to be able to process one complete annual volume of the Web of Science, we decided to calculate CSS thresholds and classes for two levels of granularity, namely sub-fields and WoS Subject Categories. With about 5% deviation, we did not find a real significance. However, we identified journals with similar impact measures but different citation profiles, independently of the granularity. Finally, we have pointed to the limitations in the choice of granularity—in terms of both too broad and too narrow subjects.

Satellite remote sensing is developing towards the micro-satellite cluster, which brings new challenges to mission assignment and planning for the cluster. A multi-agent system (MAS) is used, but the time delay caused by communication and computation is rarely considered. To solve the problem, a neural-network-based multi-granularity negotiation method under decentralized architecture is proposed. Firstly, we divided negotiation into three levels of granularity, and they work in different modes. Secondly, a neural network was trained to help the satellite select the best level in real-time. Through experiments, we compared the satellites working in three different levels of granularity, in which a multi-granularity decision was used. As a result of our experiments, a lower cost-effectiveness ratio was obtained, which proved that the multi-granularity negotiation method proposed in this paper is practical.

Companies increasingly adopt process management systems (PrMS), which offer promising perspectives for a more flexible and efficient process execution. However, for many process-aware application systems (e.g., ERP or CRM systems), the underlying process logic is still hard-coded. As a consequence, these business applications are both complex to design and costly to maintain; i.e., they require long development cycles, and even simple process changes might result in costly code adaptions and high efforts for testing. One reason for this situation stems from the fact that contemporary PrMS were primarily designed for the support of highly structured, repetitive business processes. By contrast, many processes found in practice are rather unstructured or semi-structured, i.e., they are knowledge-intensive and driven by user decisions. In addition, the business functions to be integrated with these processes usually cannot be straight-jacketed into activities. For all these reasons, the activity-centered paradigm of contemporary PrMS is by far too inflexible for realizing more advanced business applications. This deficiency mainly stems from the unsatisfactory integration of processes and data in existing PrMS. Despite emerging approaches, which target at a tighter integration of process and data, a unified and comprehensive understanding of the relationships between them is still missing. This thesis first analyzes real processes not adequately supported by existing PrMS and elaborates their characteristic properties. As a major insight it became clear that in many application scenarios comprehensive process support requires both object- and process-awareness. This means, business processes and business data must not be treated independently from each other. Instead, business processes must comply with the underlying data structure. In particular, in accordance to the given data model comprising object types and object relations, the modeling, execution and monitoring of processes must be based on two levels of granularity: object behavior and object interactions. Further, the individual processes, coordinating the behavior of single object instances, must be coordinated with the ones of related object instances. Opposed to these well-defined process support granularity levels, activities must be executable at different levels of granularity. In particular, while a particular user may only want to work on a particular object instance, another one may want to process a number of related object instances in one go. Furthermore, process execution must be accomplished in a data-driven manner; i.e., the progress of a process instance mainly depends on available business objects and on value changes of their attributes. Finally, authorized users must be able to access and manage process-related objects at any point in time. Based on the properties identified, this thesis elaborates major requirements for enabling object-awareness in processes management systems. The major contribution of the thesis is the PHILharmonicFlows framework, which addresses these requirements and enables object-aware process management in a comprehensive manner. In particular, the framework not only provides a new process modeling approach, but also establishes a well-defined operational semantics enabling the automatic and dynamic generation of end-user components for object- and process-aware business applications at run-time (e.g., overview tables and user forms). Overall, a holistic framework integrating data, processes and users offers promising perspectives in order to overcome the numerous limitations of contemporary PrMS. This thesis considers research in this area as fundamental maturation of process management technology.