The Design of an Interactive Topic Modeling Application for Media Content

Topic Modeling has been widely used by data scientists to analyze the increasing amount of text documents. Documents can be assigned to a distribution of topics with techniques like LDA or NMF, that are related to unsupervised soft clustering but consider text semantics. More recently, Interactive Topic Modeling (ITM) has been introduced to incorporate human expertise in the modeling process. This enables real-time hyperparameter optimization and topic manipulation on document and keyword level. However, current ITM applications are mostly accessible to experienced data scientists, who lack domain knowledge. Domain experts, on the other hand, usually lack the data science expertise to build and use ITM applications. This thesis presents an Interactive Topic Modeling application accessible to non-technical data analysts in the broadcasting domain. The application allows domain experts, like journalists, to explore themes in various produced media content in a dynamic, intuitive and efficient manner. An interactive interface, with an embedded NMF topic model, enables users to filter on various data sources, configure and refine the topic model, interpret and evaluate the output by visualizations, and analyze the data in wider context. This application was designed in collaboration with domain experts in focus group sessions, according to human-centered design principles. An evaluation study with ten participants shows that journalists and data analysts without any natural language processing knowledge agree that the application is not only usable, but also very user-friendly, effective and efficient. A SUS score of 81 was received, and user experience and user perceptions of control questionnaires both received an average of 4.1 on a five-point Likert scale. The ITM application thus enables this specific user group to extract meaningful topics from their produced media content, and use these results in broader perspective to perform exploratory data analysis. The success of the final application design presented in this thesis shows that the knowledge gap between data scientists and domain experts in the broadcasting field has been filled. In bigger perspective; machine learning applications can be made more accessible by translating hidden low-level details of complex models into high-level model interactions, presented in a user interface

Designing Explanation Interfaces for Transparency and Beyond

In this work-in-progress paper, we presented a participatory process of designing explanation interfaces for a social recommender system with multiple explanatory goals. We went through four stages to identify the key components of the recommendation model, expert mental model, user mental model, and target mental model. We reported the results of an online survey of current system users (N=14) and a controlled user study with a group of target users (N=15). Based on the findings, we proposed five set of explanation interfaces for five recommendation models (N=25) and discussed the user preference of the interface prototypes

Tensor approximation in visualization and graphics

In this course, we will introduce the basic concepts of tensor approximation (TA) – a higher-order generalization of the SVD and PCA methods – as well as its applications to visual data representation, analysis and visualization, and bring the TA framework closer to visualization and computer graphics researchers and practitioners. The course will cover the theoretical background of TA methods, their properties and how to compute them, as well as practical applications of TA methods in visualization and computer graphics contexts. In a first theoretical part, the attendees will be instructed on the necessary mathematical background of TA methods to learn the basics skills of using and applying these new tools in the context of the representation of large multidimensional visual data. Specific and very noteworthy features of the TA framework are highlighted which can effectively be exploited for spatio-temporal multidimensional data representation and visualization purposes. In two application oriented sessions, compact TA data representation in scientific visualization and computer graphics as well as decomposition and reconstruction algorithms will be demonstrated. At the end of the course, the participants will have a good basic knowledge of TA methods along with a practical understanding of its potential application in visualization and graphics related projects

Novelty Detection in Sequential Data by Informed Clustering and Modeling

Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can be detected by modeling normal sequences and measuring the deviations of a new sequence from the model predictions. However, in many applications data is generated by several distinct processes so that models trained on all the data tend to over-generalize and novelties remain undetected. We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately. However, this comes at a trade-off, since the amount of training data per cluster is reduced. This is a particular problem for discrete sequences where state-of-the-art models are data-hungry. The success of this approach thus depends on the quality of the clustering, i.e., whether the individual learning problems are sufficiently simpler than the joint problem. While clustering discrete sequences automatically is a challenging and domain-specific task, it is often easy for human domain experts, given the right tools. In this paper, we adapt a state-of-the-art visual analytics tool for discrete sequence clustering to obtain informed clusters from domain experts and use LSTMs to model each cluster individually. Our extensive empirical evaluation indicates that this informed clustering outperforms automatic ones and that our approach outperforms state-of-the-art novelty detection methods for discrete sequences in three real-world application scenarios. In particular, decomposition outperforms a global model despite less training data on each individual cluster

Role-Based Enterprise Mashups with State Sharing, Preservation and Restoration Support for Multi-Instance Executions

Veebimaastikul suurt populaarsust kogunud tavatarbijatele suunatud vidinapõhised veebi-rakendused on loonud soodsa pinnase üldotstarbeliste mashup’ite loomise raamistike ning tööriistade tekkeks. Need tööriistad on eelkõige suunatud tava-Interneti kasutajatele, et luua lihtsaid mashup-tüüpi rakendusi. Samal ajal oleks vidinapõhistest veebirakendustest kasu ka ärirakendustena. Peamiseks takistuseks ärirakenduste loomisel veebipõhiste raken-dustena on keerulisest äriloogikast tulenevad keerukad nõuded ning protsessid. Antud magistritöö uurib, kuidas teostada veebividinatel põhinevate mashup-tüüpi ärirakenduste arendamist nii, et säiluks mashup’ite loomisega seotud peamised eelised, lihtsus ja kiirus. Käesolev magistritöö pakub välja laienduse olemasolevale mashup-tüüpi raamistikule, et toetada mashup’i dekompositsiooni rollipõhisteks vaadeteks. Selleks jagatakse mashup väiksemateks vidinate komplektideks, tagades igale kasutajarollile komplekt just temale vajaminevatest vidinatest. Kuigi igal kasutajarollil võib olla erinev vaade kogu ärirakendusest, tagab käesolevas magistritöös pakutud lahendus suhtluse nende erinevate vaadete vahel. See on vajalik tagamaks mashup’i eksemplari ühtsust kõikide mashup’i vaadete vahel, olenemata sellest, millistest vidinatest antud kasutaja vaade koosneb. Lisaks pakub käesolev magistritöö välja lahenduse toetamaks mitut eksemplari samast vidinapõhisest ärirakendusest ning toetamaks ärirakenduse oleku salvestamist ning taastamist. Kuna ärirakendused on suunatud lahendamaks kasutajate igapäevaseid ülesandeid, on vajalik, et kasutaja saaks valida olemasolevate mashup’i eksemplaride hulgast või alustada uut eksemplari. Lisaks on vajalik, et kasutaja saaks igal ajahetkel rakenduse kasutamise lõpetada selliselt, et hiljem rakenduse kasutamist jätkates oleks tagatud sama rakenduse olek, millest kasutamine katkestati. Väljapakutud lahenduse toimimist testitakse näidisrakendusega, mis realiseerib krediidihalduse protsessi.Recent hype on consumer web mashups has resulted in many general-purpose mashup frameworks and tools. These tools aim at simplifying the creation of mashups targeted to mainstream Internet users. At the same time, mashups are also used for solving specific business-related tasks. Such mashups are called enterprise mashups and more sophisticated frameworks and tools have been developed to support their creation. However, similarly to traditional web application development tools, the complexity of these frameworks is hindering the main benefits associated with mashup development – agility and simplicity. This thesis aims at extending a general-purpose mashup framework to support develop-ment of enterprise mashups while still preserving the simplicity and agility of develop-ment. More specifically, this thesis describes a solution for role-based decomposition of mashups for multi-instance executions with state sharing, preservation and restoration. In this thesis, a general-purpose mashup framework is extended with the concept of roles to support multi-user interaction and decomposing complex enterprise mashups with rich interactions into role-based views. In the context of this thesis, a view is defined as a subset of widgets a mashup is made of. Hence, through views an effective mechanism is provided for decomposing enterprise mashups to mashups as simple as general-purpose mashups. Additionally, this thesis proposes a generic solution for multi-instance mashup executions. In this thesis, each workflow instance is associated with an instance of a mashup. Since situational applications target at solving users day-to-day tasks, it is necessary to support multiple instances of a mashup. Furthermore, support for multiple mashup instances leverages users’ ability to participate in multiple workflow instances and to initialize new ones. Such mashup instances are in this thesis also referred to as mashup sessions. Finally, a solution is proposed for supporting mashup state sharing, preservation and restoration. Sharing states with other users is the key mechanism for facilitating interaction and collaboration between multiple users. State preservation and restoration are needed to allow a user to stop using the mashup and to resume to the same state at a later time. The proposed solution is also validated through a proof of concept application