14 research outputs found
Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation
Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications
throughout the world, the amount of data stored on the Web has been
growing exponentially. In this new digital era, a large number of companies
have emerged with the purpose of ltering the information available on the
web and provide users with interesting items. The algorithms and models
used to recommend these items are called Recommender Systems. These
systems are applied to a large number of domains, from music, books, or
movies to dating or Point-of-Interest (POI), which is an increasingly popular
domain where users receive recommendations of di erent places when
they arrive to a city.
In this thesis, we focus on exploiting the use of contextual information, especially
temporal and sequential data, and apply it in novel ways in both
traditional and Point-of-Interest recommendation. We believe that this type
of information can be used not only for creating new recommendation models
but also for developing new metrics for analyzing the quality of these
recommendations. In one of our rst contributions we propose di erent
metrics, some of them derived from previously existing frameworks, using
this contextual information. Besides, we also propose an intuitive algorithm
that is able to provide recommendations to a target user by exploiting the
last common interactions with other similar users of the system.
At the same time, we conduct a comprehensive review of the algorithms
that have been proposed in the area of POI recommendation between 2011
and 2019, identifying the common characteristics and methodologies used.
Once this classi cation of the algorithms proposed to date is completed, we
design a mechanism to recommend complete routes (not only independent
POIs) to users, making use of reranking techniques. In addition, due to the
great di culty of making recommendations in the POI domain, we propose
the use of data aggregation techniques to use information from di erent
cities to generate POI recommendations in a given target city.
In the experimental work we present our approaches on di erent datasets
belonging to both classical and POI recommendation. The results obtained
in these experiments con rm the usefulness of our recommendation proposals,
in terms of ranking accuracy and other dimensions like novelty, diversity,
and coverage, and the appropriateness of our metrics for analyzing temporal
information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones
en todo el mundo, la cantidad de datos almacenados en la red ha crecido
exponencialmente. En esta nueva era digital, han surgido un gran n umero
de empresas con el objetivo de ltrar la informaci on disponible en la red
y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos
utilizados para recomendar estos art culos reciben el nombre de Sistemas de
Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios,
desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs,
en ingl es), un dominio cada vez m as popular en el que los usuarios reciben
recomendaciones de diferentes lugares cuando llegan a una ciudad.
En esta tesis, nos centramos en explotar el uso de la informaci on contextual,
especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa
tanto en la recomendaci on cl asica como en la recomendaci on de POIs.
Creemos que este tipo de informaci on puede utilizarse no s olo para crear
nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas
m etricas para analizar la calidad de estas recomendaciones. En una de
nuestras primeras contribuciones proponemos diferentes m etricas, algunas
derivadas de formulaciones previamente existentes, utilizando esta informaci
on contextual. Adem as, proponemos un algoritmo intuitivo que es
capaz de proporcionar recomendaciones a un usuario objetivo explotando
las ultimas interacciones comunes con otros usuarios similares del sistema.
Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que
se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y
2019, identi cando las caracter sticas comunes y las metodolog as utilizadas.
Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la
fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo
POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking.
Adem as, debido a la gran di cultad de realizar recomendaciones en el
ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos
para utilizar la informaci on de diferentes ciudades y generar recomendaciones
de POIs en una determinada ciudad objetivo.
En el trabajo experimental presentamos nuestros m etodos en diferentes
conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los
resultados obtenidos en estos experimentos con rman la utilidad de nuestras
propuestas de recomendaci on en t erminos de precisi on de ranking y de
otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de
apropiadas son nuestras m etricas para analizar la informaci on temporal y
los sesgos en las recomendaciones producida
Multi-sensor data fusion in mobile devices for the identification of Activities of Daily Living
Following the recent advances in technology and the growing use of mobile devices such as
smartphones, several solutions may be developed to improve the quality of life of users in the
context of Ambient Assisted Living (AAL). Mobile devices have different available sensors, e.g.,
accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS)
receiver, which allow the acquisition of physical and physiological parameters for the
recognition of different Activities of Daily Living (ADL) and the environments in which they are
performed. The definition of ADL includes a well-known set of tasks, which include basic selfcare
tasks, based on the types of skills that people usually learn in early childhood, including
feeding, bathing, dressing, grooming, walking, running, jumping, climbing stairs, sleeping,
watching TV, working, listening to music, cooking, eating and others. On the context of AAL,
some individuals (henceforth called user or users) need particular assistance, either because
the user has some sort of impairment, or because the user is old, or simply because users
need/want to monitor their lifestyle. The research and development of systems that provide a
particular assistance to people is increasing in many areas of application. In particular, in the
future, the recognition of ADL will be an important element for the development of a personal
digital life coach, providing assistance to different types of users. To support the recognition
of ADL, the surrounding environments should be also recognized to increase the reliability of
these systems.
The main focus of this Thesis is the research on methods for the fusion and classification of the
data acquired by the sensors available in off-the-shelf mobile devices in order to recognize ADL
in almost real-time, taking into account the large diversity of the capabilities and
characteristics of the mobile devices available in the market. In order to achieve this objective,
this Thesis started with the review of the existing methods and technologies to define the
architecture and modules of the method for the identification of ADL. With this review and
based on the knowledge acquired about the sensors available in off-the-shelf mobile devices,
a set of tasks that may be reliably identified was defined as a basis for the remaining research
and development to be carried out in this Thesis. This review also identified the main stages
for the development of a new method for the identification of the ADL using the sensors
available in off-the-shelf mobile devices; these stages are data acquisition, data processing,
data cleaning, data imputation, feature extraction, data fusion and artificial intelligence. One
of the challenges is related to the different types of data acquired from the different sensors,
but other challenges were found, including the presence of environmental noise, the positioning
of the mobile device during the daily activities, the limited capabilities of the mobile devices
and others. Based on the acquired data, the processing was performed, implementing data
cleaning and feature extraction methods, in order to define a new framework for the recognition of ADL. The data imputation methods were not applied, because at this stage of
the research their implementation does not have influence in the results of the identification
of the ADL and environments, as the features are extracted from a set of data acquired during
a defined time interval and there are no missing values during this stage. The joint selection of
the set of usable sensors and the identifiable set of tasks will then allow the development of a
framework that, considering multi-sensor data fusion technologies and context awareness, in
coordination with other information available from the user context, such as his/her agenda
and the time of the day, will allow to establish a profile of the tasks that the user performs in
a regular activity day. The classification method and the algorithm for the fusion of the features
for the recognition of ADL and its environments needs to be deployed in a machine with some
computational power, while the mobile device that will use the created framework, can
perform the identification of the ADL using a much less computational power. Based on the
results reported in the literature, the method chosen for the recognition of the ADL is composed
by three variants of Artificial Neural Networks (ANN), including simple Multilayer Perceptron
(MLP) networks, Feedforward Neural Networks (FNN) with Backpropagation, and Deep Neural
Networks (DNN).
Data acquisition can be performed with standard methods. After the acquisition, the data must
be processed at the data processing stage, which includes data cleaning and feature extraction
methods. The data cleaning method used for motion and magnetic sensors is the low pass filter,
in order to reduce the noise acquired; but for the acoustic data, the Fast Fourier Transform
(FFT) was applied to extract the different frequencies. When the data is clean, several features
are then extracted based on the types of sensors used, including the mean, standard deviation,
variance, maximum value, minimum value and median of raw data acquired from the motion
and magnetic sensors; the mean, standard deviation, variance and median of the maximum
peaks calculated with the raw data acquired from the motion and magnetic sensors; the five
greatest distances between the maximum peaks calculated with the raw data acquired from
the motion and magnetic sensors; the mean, standard deviation, variance, median and 26 Mel-
Frequency Cepstral Coefficients (MFCC) of the frequencies obtained with FFT based on the raw
data acquired from the microphone data; and the distance travelled calculated with the data
acquired from the GPS receiver. After the extraction of the features, these will be grouped in
different datasets for the application of the ANN methods and to discover the method and
dataset that reports better results. The classification stage was incrementally developed,
starting with the identification of the most common ADL (i.e., walking, running, going upstairs,
going downstairs and standing activities) with motion and magnetic sensors. Next, the
environments were identified with acoustic data, i.e., bedroom, bar, classroom, gym, kitchen,
living room, hall, street and library. After the environments are recognized, and based on the
different sets of sensors commonly available in the mobile devices, the data acquired from the
motion and magnetic sensors were combined with the recognized environment in order to
differentiate some activities without motion, i.e., sleeping and watching TV. The number of recognized activities in this stage was increased with the use of the distance travelled,
extracted from the GPS receiver data, allowing also to recognize the driving activity.
After the implementation of the three classification methods with different numbers of
iterations, datasets and remaining configurations in a machine with high processing
capabilities, the reported results proved that the best method for the recognition of the most
common ADL and activities without motion is the DNN method, but the best method for the
recognition of environments is the FNN method with Backpropagation. Depending on the
number of sensors used, this implementation reports a mean accuracy between 85.89% and
89.51% for the recognition of the most common ADL, equals to 86.50% for the recognition of
environments, and equals to 100% for the recognition of activities without motion, reporting
an overall accuracy between 85.89% and 92.00%.
The last stage of this research work was the implementation of the structured framework for
the mobile devices, verifying that the FNN method requires a high processing power for the
recognition of environments and the results reported with the mobile application are lower
than the results reported with the machine with high processing capabilities used. Thus, the
DNN method was also implemented for the recognition of the environments with the mobile
devices. Finally, the results reported with the mobile devices show an accuracy between 86.39%
and 89.15% for the recognition of the most common ADL, equal to 45.68% for the recognition
of environments, and equal to 100% for the recognition of activities without motion, reporting
an overall accuracy between 58.02% and 89.15%.
Compared with the literature, the results returned by the implemented framework show only
a residual improvement. However, the results reported in this research work comprehend the
identification of more ADL than the ones described in other studies. The improvement in the
recognition of ADL based on the mean of the accuracies is equal to 2.93%, but the maximum
number of ADL and environments previously recognized was 13, while the number of ADL and
environments recognized with the framework resulting from this research is 16. In conclusion,
the framework developed has a mean improvement of 2.93% in the accuracy of the recognition
for a larger number of ADL and environments than previously reported.
In the future, the achievements reported by this PhD research may be considered as a start
point of the development of a personal digital life coach, but the number of ADL and
environments recognized by the framework should be increased and the experiments should be
performed with different types of devices (i.e., smartphones and smartwatches), and the data
imputation and other machine learning methods should be explored in order to attempt to
increase the reliability of the framework for the recognition of ADL and its environments.Após os recentes avanços tecnológicos e o crescente uso dos dispositivos móveis, como por
exemplo os smartphones, várias soluções podem ser desenvolvidas para melhorar a qualidade
de vida dos utilizadores no contexto de Ambientes de Vida Assistida (AVA) ou Ambient Assisted
Living (AAL). Os dispositivos móveis integram vários sensores, tais como acelerómetro,
giroscópio, magnetómetro, microfone e recetor de Sistema de Posicionamento Global (GPS),
que permitem a aquisição de vários parâmetros físicos e fisiológicos para o reconhecimento de
diferentes Atividades da Vida Diária (AVD) e os seus ambientes. A definição de AVD inclui um
conjunto bem conhecido de tarefas que são tarefas básicas de autocuidado, baseadas nos tipos
de habilidades que as pessoas geralmente aprendem na infância. Essas tarefas incluem
alimentar-se, tomar banho, vestir-se, fazer os cuidados pessoais, caminhar, correr, pular, subir
escadas, dormir, ver televisão, trabalhar, ouvir música, cozinhar, comer, entre outras. No
contexto de AVA, alguns indivíduos (comumente chamados de utilizadores) precisam de
assistência particular, seja porque o utilizador tem algum tipo de deficiência, seja porque é
idoso, ou simplesmente porque o utilizador precisa/quer monitorizar e treinar o seu estilo de
vida. A investigação e desenvolvimento de sistemas que fornecem algum tipo de assistência
particular está em crescente em muitas áreas de aplicação. Em particular, no futuro, o
reconhecimento das AVD é uma parte importante para o desenvolvimento de um assistente
pessoal digital, fornecendo uma assistência pessoal de baixo custo aos diferentes tipos de
pessoas. pessoas. Para ajudar no reconhecimento das AVD, os ambientes em que estas se
desenrolam devem ser reconhecidos para aumentar a fiabilidade destes sistemas.
O foco principal desta Tese é o desenvolvimento de métodos para a fusão e classificação dos
dados adquiridos a partir dos sensores disponíveis nos dispositivos móveis, para o
reconhecimento quase em tempo real das AVD, tendo em consideração a grande diversidade
das características dos dispositivos móveis disponíveis no mercado. Para atingir este objetivo,
esta Tese iniciou-se com a revisão dos métodos e tecnologias existentes para definir a
arquitetura e os módulos do novo método de identificação das AVD. Com esta revisão da
literatura e com base no conhecimento adquirido sobre os sensores disponíveis nos dispositivos
móveis disponíveis no mercado, um conjunto de tarefas que podem ser identificadas foi
definido para as pesquisas e desenvolvimentos desta Tese. Esta revisão também identifica os
principais conceitos para o desenvolvimento do novo método de identificação das AVD,
utilizando os sensores, são eles: aquisição de dados, processamento de dados, correção de
dados, imputação de dados, extração de características, fusão de dados e extração de
resultados recorrendo a métodos de inteligência artificial. Um dos desafios está relacionado
aos diferentes tipos de dados adquiridos pelos diferentes sensores, mas outros desafios foram
encontrados, sendo os mais relevantes o ruído ambiental, o posicionamento do dispositivo durante a realização das atividades diárias, as capacidades limitadas dos dispositivos móveis.
As diferentes características das pessoas podem igualmente influenciar a criação dos métodos,
escolhendo pessoas com diferentes estilos de vida e características físicas para a aquisição e
identificação dos dados adquiridos a partir de sensores. Com base nos dados adquiridos,
realizou-se o processamento dos dados, implementando-se métodos de correção dos dados e a
extração de características, para iniciar a criação do novo método para o reconhecimento das
AVD. Os métodos de imputação de dados foram excluídos da implementação, pois não iriam
influenciar os resultados da identificação das AVD e dos ambientes, na medida em que são
utilizadas as características extraídas de um conjunto de dados adquiridos durante um intervalo
de tempo definido.
A seleção dos sensores utilizáveis, bem como das AVD identificáveis, permitirá o
desenvolvimento de um método que, considerando o uso de tecnologias para a fusão de dados
adquiridos com múltiplos sensores em coordenação com outras informações relativas ao
contexto do utilizador, tais como a agenda do utilizador, permitindo estabelecer um perfil de
tarefas que o utilizador realiza diariamente. Com base nos resultados obtidos na literatura, o
método escolhido para o reconhecimento das AVD são as diferentes variantes das Redes
Neuronais Artificiais (RNA), incluindo Multilayer Perceptron (MLP), Feedforward Neural
Networks (FNN) with Backpropagation and Deep Neural Networks (DNN). No final, após a
criação dos métodos para cada fase do método para o reconhecimento das AVD e ambientes, a
implementação sequencial dos diferentes métodos foi realizada num dispositivo móvel para
testes adicionais.
Após a definição da estrutura do método para o reconhecimento de AVD e ambientes usando
dispositivos móveis, verificou-se que a aquisição de dados pode ser realizada com os métodos
comuns. Após a aquisição de dados, os mesmos devem ser processados no módulo de
processamento de dados, que inclui os métodos de correção de dados e de extração de
características. O método de correção de dados utilizado para sensores de movimento e
magnéticos é o filtro passa-baixo de modo a reduzir o ruído, mas para os dados acústicos, a
Transformada Rápida de Fourier (FFT) foi aplicada para extrair as diferentes frequências.
Após a correção dos dados, as diferentes características foram extraídas com base nos tipos de
sensores usados, sendo a média, desvio padrão, variância, valor máximo, valor mínimo e
mediana de dados adquiridos pelos sensores magnéticos e de movimento, a média, desvio
padrão, variância e mediana dos picos máximos calculados com base nos dados adquiridos pelos
sensores magnéticos e de movimento, as cinco maiores distâncias entre os picos máximos
calculados com os dados adquiridos dos sensores de movimento e magnéticos, a média, desvio
padrão, variância e 26 Mel-Frequency Cepstral Coefficients (MFCC) das frequências obtidas
com FFT com base nos dados obtidos a partir do microfone, e a distância calculada com os
dados adquiridos pelo recetor de GPS. Após a extração das características, as mesmas são agrupadas em diferentes conjuntos de dados
para a aplicação dos métodos de RNA de modo a descobrir o método e o conjunto de
características que reporta melhores resultados. O módulo de classificação de dados foi
incrementalmente desenvolvido, começando com a identificação das AVD comuns com sensores
magnéticos e de movimento, i.e., andar, correr, subir escadas, descer escadas e parado. Em
seguida, os ambientes são identificados com dados de sensores acústicos, i.e., quarto, bar, sala
de aula, ginásio, cozinha, sala de estar, hall, rua e biblioteca. Com base nos ambientes
reconhecidos e os restantes sensores disponíveis nos dispositivos móveis, os dados adquiridos
dos sensores magnéticos e de movimento foram combinados com o ambiente reconhecido para
diferenciar algumas atividades sem movimento (i.e., dormir e ver televisão), onde o número
de atividades reconhecidas nesta fase aumenta com a fusão da distância percorrida, extraída
a partir dos dados do recetor GPS, permitindo também reconhecer a atividade de conduzir.
Após a implementação dos três métodos de classificação com diferentes números de iterações,
conjuntos de dados e configurações numa máquina com alta capacidade de processamento, os
resultados relatados provaram que o melhor método para o reconhecimento das atividades
comuns de AVD e atividades sem movimento é o método DNN, mas o melhor método para o
reconhecimento de ambientes é o método FNN with Backpropagation. Dependendo do número
de sensores utilizados, esta implementação reporta uma exatidão média entre 85,89% e 89,51%
para o reconhecimento das AVD comuns, igual a 86,50% para o reconhecimento de ambientes,
e igual a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão
global entre 85,89% e 92,00%.
A última etapa desta Tese foi a implementação do método nos dispositivos móveis, verificando
que o método FNN requer um alto poder de processamento para o reconhecimento de
ambientes e os resultados reportados com estes dispositivos são inferiores aos resultados
reportados com a máquina com alta capacidade de processamento utilizada no
desenvolvimento do método. Assim, o método DNN foi igualmente implementado para o
reconhecimento dos ambientes com os dispositivos móveis. Finalmente, os resultados relatados
com os dispositivos móveis reportam uma exatidão entre 86,39% e 89,15% para o
reconhecimento das AVD comuns, igual a 45,68% para o reconhecimento de ambientes, e igual
a 100% para o reconhecimento de atividades sem movimento, reportando uma exatidão geral
entre 58,02% e 89,15%.
Com base nos resultados relatados na literatura, os resultados do método desenvolvido mostram
uma melhoria residual, mas os resultados desta Tese identificam mais AVD que os demais
estudos disponíveis na literatura. A melhoria no reconhecimento das AVD com base na média
das exatidões é igual a 2,93%, mas o número máximo de AVD e ambientes reconhecidos pelos
estudos disponíveis na literatura é 13, enquanto o número de AVD e ambientes reconhecidos
com o método implementado é 16. Assim, o método desenvolvido tem uma melhoria de 2,93%
na exatidão do reconhecimento num maior número de AVD e ambientes. Como trabalho futuro, os resultados reportados nesta Tese podem ser considerados um ponto
de partida para o desenvolvimento de um assistente digital pessoal, mas o número de ADL e
ambientes reconhecidos pelo método deve ser aumentado e as experiências devem ser
repetidas com diferentes tipos de dispositivos móveis (i.e., smartphones e smartwatches), e os
métodos de imputação e outros métodos de classificação de dados devem ser explorados de
modo a tentar aumentar a confiabilidade do método para o reconhecimento das AVD e
ambientes
Actes des 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018
International audienc
Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices
Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.This work was supported by FCT project UID/EEA/50008/2013. The authors would also like to acknowledge the contribution of the COST Action IC1303–AAPELE–Architectures, Algorithms and Protocols for Enhanced Living Environments
筑波大学計算科学研究センター 平成27年度 年次報告書
まえがき …… 21 センター組織と構成員 …… 32 平成 27 年度の活動状況 …… 73 特色ある共同研究活動 …… 74 研究者コミュニティへの貢献 …… 95 各研究部門の報告 …… 10I. 素粒子物理研究部門 …… 10II. 宇宙物理研究部門 …… 42III.原子核物理研究部門 …… 66IV. 量子物性研究部門 …… 86V. 生命科学研究部門 …… 105 V-1. 生命機能情報分野 …… 105 V-2. 分子進化分野 …… 120VI. 地球環境研究部門 …… 135VII.高性能計算システム研究部門 …… 148VIII. 計算情報学研究部門 …… 193 Ⅷ-1. データ基盤分野 …… 193 Ⅷ-2. 計算メディア分野 …… 21
Untersuchungen zur Risikominimierungstechnik Stealth Computing für verteilte datenverarbeitende Software-Anwendungen mit nutzerkontrollierbar zusicherbaren Eigenschaften
Die Sicherheit und Zuverlässigkeit von Anwendungen, welche schutzwürdige Daten verarbeiten, lässt sich durch die geschützte Verlagerung in die Cloud mit einer Kombination aus zielgrößenabhängiger Datenkodierung, kontinuierlicher mehrfacher Dienstauswahl, dienstabhängiger optimierter Datenverteilung und kodierungsabhängiger Algorithmen deutlich erhöhen und anwenderseitig kontrollieren. Die Kombination der Verfahren zu einer anwendungsintegrierten Stealth-Schutzschicht ist eine notwendige Grundlage für die Konstruktion sicherer Anwendungen mit zusicherbaren Sicherheitseigenschaften im Rahmen eines darauf angepassten Softwareentwicklungsprozesses.:1 Problemdarstellung
1.1 Einführung
1.2 Grundlegende Betrachtungen
1.3 Problemdefinition
1.4 Einordnung und Abgrenzung
2 Vorgehensweise und Problemlösungsmethodik
2.1 Annahmen und Beiträge
2.2 Wissenschaftliche Methoden
2.3 Struktur der Arbeit
3 Stealth-Kodierung für die abgesicherte Datennutzung
3.1 Datenkodierung
3.2 Datenverteilung
3.3 Semantische Verknüpfung verteilter kodierter Daten
3.4 Verarbeitung verteilter kodierter Daten
3.5 Zusammenfassung der Beiträge
4 Stealth-Konzepte für zuverlässige Dienste und Anwendungen
4.1 Überblick über Plattformkonzepte und -dienste
4.2 Netzwerkmultiplexerschnittstelle
4.3 Dateispeicherschnittstelle
4.4 Datenbankschnittstelle
4.5 Stromspeicherdienstschnittstelle
4.6 Ereignisverarbeitungsschnittstelle
4.7 Dienstintegration
4.8 Entwicklung von Anwendungen
4.9 Plattformäquivalente Cloud-Integration sicherer Dienste und Anwendungen
4.10 Zusammenfassung der Beiträge
5 Szenarien und Anwendungsfelder
5.1 Online-Speicherung von Dateien mit Suchfunktion
5.2 Persönliche Datenanalyse
5.3 Mehrwertdienste für das Internet der Dinge
6 Validierung
6.1 Infrastruktur für Experimente
6.2 Experimentelle Validierung der Datenkodierung
6.3 Experimentelle Validierung der Datenverteilung
6.4 Experimentelle Validierung der Datenverarbeitung
6.5 Funktionstüchtigkeit und Eigenschaften der Speicherdienstanbindung
6.6 Funktionstüchtigkeit und Eigenschaften der Speicherdienstintegration
6.7 Funktionstüchtigkeit und Eigenschaften der Datenverwaltung
6.8 Funktionstüchtigkeit und Eigenschaften der Datenstromverarbeitung
6.9 Integriertes Szenario: Online-Speicherung von Dateien
6.10 Integriertes Szenario: Persönliche Datenanalyse
6.11 Integriertes Szenario: Mobile Anwendungen für das Internet der Dinge
7 Zusammenfassung
7.1 Zusammenfassung der Beiträge
7.2 Kritische Diskussion und Bewertung
7.3 Ausblick
Verzeichnisse
Tabellenverzeichnis
Abbildungsverzeichnis
Listings
Literaturverzeichnis
Symbole und Notationen
Software-Beiträge für native Cloud-Anwendungen
Repositorien mit ExperimentdatenThe security and reliability of applications processing sensitive data can be significantly increased and controlled by the user by a combination of techniques. These encompass a targeted data coding, continuous multiple service selection, service-specific optimal data distribution and coding-specific algorithms. The combination of the techniques towards an application-integrated stealth protection layer is a necessary precondition for the construction of safe applications with guaranteeable safety properties in the context of a custom software development process.:1 Problemdarstellung
1.1 Einführung
1.2 Grundlegende Betrachtungen
1.3 Problemdefinition
1.4 Einordnung und Abgrenzung
2 Vorgehensweise und Problemlösungsmethodik
2.1 Annahmen und Beiträge
2.2 Wissenschaftliche Methoden
2.3 Struktur der Arbeit
3 Stealth-Kodierung für die abgesicherte Datennutzung
3.1 Datenkodierung
3.2 Datenverteilung
3.3 Semantische Verknüpfung verteilter kodierter Daten
3.4 Verarbeitung verteilter kodierter Daten
3.5 Zusammenfassung der Beiträge
4 Stealth-Konzepte für zuverlässige Dienste und Anwendungen
4.1 Überblick über Plattformkonzepte und -dienste
4.2 Netzwerkmultiplexerschnittstelle
4.3 Dateispeicherschnittstelle
4.4 Datenbankschnittstelle
4.5 Stromspeicherdienstschnittstelle
4.6 Ereignisverarbeitungsschnittstelle
4.7 Dienstintegration
4.8 Entwicklung von Anwendungen
4.9 Plattformäquivalente Cloud-Integration sicherer Dienste und Anwendungen
4.10 Zusammenfassung der Beiträge
5 Szenarien und Anwendungsfelder
5.1 Online-Speicherung von Dateien mit Suchfunktion
5.2 Persönliche Datenanalyse
5.3 Mehrwertdienste für das Internet der Dinge
6 Validierung
6.1 Infrastruktur für Experimente
6.2 Experimentelle Validierung der Datenkodierung
6.3 Experimentelle Validierung der Datenverteilung
6.4 Experimentelle Validierung der Datenverarbeitung
6.5 Funktionstüchtigkeit und Eigenschaften der Speicherdienstanbindung
6.6 Funktionstüchtigkeit und Eigenschaften der Speicherdienstintegration
6.7 Funktionstüchtigkeit und Eigenschaften der Datenverwaltung
6.8 Funktionstüchtigkeit und Eigenschaften der Datenstromverarbeitung
6.9 Integriertes Szenario: Online-Speicherung von Dateien
6.10 Integriertes Szenario: Persönliche Datenanalyse
6.11 Integriertes Szenario: Mobile Anwendungen für das Internet der Dinge
7 Zusammenfassung
7.1 Zusammenfassung der Beiträge
7.2 Kritische Diskussion und Bewertung
7.3 Ausblick
Verzeichnisse
Tabellenverzeichnis
Abbildungsverzeichnis
Listings
Literaturverzeichnis
Symbole und Notationen
Software-Beiträge für native Cloud-Anwendungen
Repositorien mit Experimentdate
Social informatics
5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p
Leveraging query logs for user-centric OLAP
OLAP (On-Line Analytical Processing), the process of efficiently enabling common analytical operations on the multidimensional view of data, is a corner stone of Business Intelligence.While OLAP is now a mature, efficiently implemented technology, very little attention has been paid to the effectiveness of the analysis and the user-friendliness of this technology, often considered tedious of use.This dissertation is a contribution to developing user-centric OLAP, focusing on the use of former queries logged by an OLAP server to enhance subsequent analyses. It shows how logs of OLAP queries can be modeled, constructed, manipulated, compared, and finally leveraged for personalization and recommendation.Logs are modeled as sets of analytical sessions, sessions being modeled as sequences of OLAP queries. Three main approaches are presented for modeling queries: as unevaluated collections of fragments (e.g., group by sets, sets of selection predicates, sets of measures), as sets of references obtained by partially evaluating the query over dimensions, or as query answers. Such logs can be constructed even from sets of SQL query expressions, by translating these expressions into a multidimensional algebra, and bridging the translations to detect analytical sessions. Logs can be searched, filtered, compared, combined, modified and summarized with a language inspired by the relational algebra and parametrized by binary relations over sessions. In particular, these relations can be specialization relations or based on similarity measures tailored for OLAP queries and analytical sessions. Logs can be mined for various hidden knowledge, that, depending on the query model used, accurately represents the user behavior extracted.This knowledge includes simple preferences, navigational habits and discoveries made during former explorations,and can be it used in various query personalization or query recommendation approaches.Such approaches vary in terms of formulation effort, proactiveness, prescriptiveness and expressive power:query personalization, i.e., coping with a current query too few or too many results, can use dedicated operators for expressing preferences, or be based on query expansion;query recommendation, i.e., suggesting queries to pursue an analytical session,can be based on information extracted from the current state of the database and the query, or be purely history based, i.e., leveraging the query log.While they can be immediately integrated into a complete architecture for User-Centric Query Answering in data warehouses, the models and approaches introduced in this dissertation can also be seen as a starting point for assessing the effectiveness of analytical sessions, with the ultimate goal to enhance the overall decision making process