Analysis and Use of MapReduce for Recommender Systems

MapReduce je programski model, namenjen za razvoj skalabilnih paralelnih aplikacij za obdelavo velikih množic podatkov, izvajalno okolje, ki podpira programski model in koordinira izvajanje programov, in implementacija programskega modela in izvajalnega okolja. Cilj diplomskega dela je analizirati MapReduce in ga preizkusiti na dveh primerih priporočilnih sistemov. Cilj smo dosegli, saj smo uspeli realizirati izračun s pomočjo MapReduce na testnih primerih. Najprej smo analizirali programski model in izvajalno okolje ter primerjali tri implementacije MapReduce: Hadoop MapReduce, MongoDB in knjižnico MapReduce-MPI. Ugotovili smo, da je za realizacijo izbranih primerov priporočilnih sistemov najprimernejša implementacija Hadoop MapReduce, saj nudi toleranco za okvare in reproducira podatke, s čimer zagotavlja zanesljivost. Nato smo z uporabo navidezne naprave Cloudera QuickStart VM, ki je gruča Hadoop z enim vozliščem, realizirali izbrana primera priporočilnih sistemov.MapReduce is a programming model for developing scalable parallel applications for processing large data sets, an execution framework that supports the programming model and coordinates the execution of programs and an implementation of the programming model and the execution framework. The goal of the thesis is to analyse MapReduce and to use it on two examples of recommender systems. The goal is achieved by developing the computation with MapReduce successfully. At first the programming model and the execution framework are analysed and three implementations for MapReduce: Hadoop MapReduce, MongoDB and MapReduce-MPI Library are compared. It is discovered that Hadoop MapReduce is the most suitable implementation for developing the selected examples of recommender systems as it provides fault tolerance and data reproduction which ensure reliability. Then the selected examples of recommender systems are developed using Cloudera QuickStart VM which is a one node Hadoop cluster

Analysis and Use of MapReduce for Recommender Systems

MapReduce is a programming model for developing scalable parallel applications for processing large data sets, an execution framework that supports the programming model and coordinates the execution of programs and an implementation of the programming model and the execution framework. The goal of the thesis is to analyse MapReduce and to use it on two examples of recommender systems. The goal is achieved by developing the computation with MapReduce successfully. At first the programming model and the execution framework are analysed and three implementations for MapReduce: Hadoop MapReduce, MongoDB and MapReduce-MPI Library are compared. It is discovered that Hadoop MapReduce is the most suitable implementation for developing the selected examples of recommender systems as it provides fault tolerance and data reproduction which ensure reliability. Then the selected examples of recommender systems are developed using Cloudera QuickStart VM which is a one node Hadoop cluster

Model za oceno dolžine koraka z inercijskimi senzorji

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. Besides, there is an absence of performance evaluation protocols when dealing with the analysis and comparison of the models. The main scientific contributions of this doctoral dissertation encompass a new approach to performance evaluation of the models and two improved inertial-sensor-based step length estimation models that estimated step length more accurately than related models selected for comparison. Both models were designed considering measured stride lengths and kinematics of the human body during walking. We present two step length estimation models herein. The first model utilizes acceleration magnitude as the predominant input, whereas the second model also includes step frequency. To the best of our knowledge, we were the first to employ principal component analysis and canonical correlation analysis to characterize the acquired experimental data that included spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system. We evaluated the performance of the proposed models for four common smartphone positions and walking on a treadmill and a rectangular-shaped test polygon. Both models yielded promising results, i.e., overall mean absolute stride length estimation errors of 6.44 cm and 5.64 cm, respectively. On average, the first model achieved a mean absolute error (MAE) of stride length estimation approximately 27% less than the average MAEs produced by the related models included in the comparison. Whereas the second model achieved an MAE of stride length estimation approximately 26% less than the MAEs of the related models included in the comparison on average. Both models are unaffected by smartphone orientation, having the advantage that no special care regarding orientation being needed when attaching the smartphone to a particular body segment. Due to promising results and favorable characteristics, both models could present an appealing alternative for step length estimation in PDR-based approaches. During this research, we also started setting the basis for standardizing the performance evaluation procedure by dealing with an in-depth analysis and comparison of step length estimation models, proposing the following categories of models: step-frequency-based, acceleration-based, angle-based, and multiparameter. Furthermore, we investigated the evaluation approaches of step length estimation models and extracted the evaluation guidelines considering several criteria. In the scope of this work, we also established an open benchmark repository including over 70 km of gait measurements obtained from a group of healthy adults. This repository fosters the comparability of the evaluation results and simplifies the benchmarking of new models. To the best of our knowledge, we were the first to introduce this way of comparison of the models, which has the potential to become a generalized and accepted way of evaluating and comparing performances of step length estimation models.Računska navigacija z inercijskimi senzorji uporablja številne pristope za oceno dolžine koraka, zlasti modele, ki so namenjeni oceni dolžine koraka na pametnih telefonih. Vendar pa avtorji modelov pri njihovi zasnovi redko uporabijo izmerjene dolžine korakov in kinematiko človeškega telesa med hojo. Prav tako ni uveljavljenega protokola za ovrednotenje modelov, kar še posebej pride do izraza pri njihovi analizi in primerjavi. Glavni znanstveno raziskovalni doprinosi te doktorske disertacije zajemajo nov pristop za ovrednotenje modelov ter dva nova izboljšana modela za oceno dolžine koraka z inercijskimi senzorji, ki dosegata boljše rezultate od primerljivih modelov ter sta zasnovana upoštevajoč izmerjene dolžine ciklov korakov in kinematiko gibanja človeškega telesa med hojo. Prvi predlagani model temelji na magnitudi pospeška, drugi pa na magnitudi pospeška in frekvenci korakov. Za karakterizacijo zbranih eksperimentalnih podatkov pri izpeljavi modelov smo kot prvi uporabili analizo glavnih komponent in kanonično korelacijsko analizo, pri čemer smo se osredotočili na prostorske položaje referenčnih točk na človeškem telesu med hojo, ki jih je spremljal optični merilni sistem. Predlagana modela smo ovrednotili za štiri tipične položaje pametnega telefona ter hojo po tekalni stezi in pravokotnem testnem poligonu. Oba modela sta dosegla obetavne rezultate. Skupna povprečna absolutna napaka ocene dolžine ciklov korakov je znašala 6.44 cm za prvi model oziroma 5.64 cm za drugi model. Prvi model je na enaki množici podatkov v povprečju dosegel približno 27% manjšo skupno povprečno absolutno napako ocene dolžine ciklov korakov kot modeli, ki smo jih vključili v njegovo primerjalno analizo. Drugi model pa je v povprečju dosegel približno 26% manjšo skupno povprečno absolutno napako ocene dolžine ciklov korakov kot modeli, ki smo jih vključili v njegovo primerjalno analizo. Posledično predlagana modela predstavljata atraktivno alternativo za oceno dolžine koraka pri računski navigaciji, saj orientacija pametnega telefona na njiju ne vpliva in zato ni potrebno posebne pozornosti nameniti usmerjenosti pametnega telefona pri namestitvi na določen del telesa, kar predstavlja pomembno prednost v primerjavi s številnimi drugimi modeli. Med to raziskavo smo začeli postavljati tudi osnove za standardizacijo ovrednotenja modelov za oceno dolžine koraka. Na podlagi poglobljene analize in primerjave smo predlagali nove kategorije modelov: modeli, zasnovani na frekvenci koraka, modeli, zasnovani na pospešku, modeli, zasnovani na kotu, in multiparametrični modeli. Poleg tega smo raziskali obstoječe pristope ovrednotenja modelov za oceno dolžine koraka in izluščili smernice za ovrednotenje ob upoštevanju več kriterijev. V okviru tega dela smo za primerjavo vzpostavili javno dostopen referenčni repozitorij z več kot 70 km meritev hoje zdravih odraslih oseb. Ta repozitorij spodbuja primerljivost rezultatov ovrednotenja in poenostavlja primerjalno analizo novih modelov. Kot prvi smo začeli z aktivnostmi za vzpostavitev takega načina primerjave, ki ima potencial, da postane splošno sprejet način za ovrednotenje in primerjavo performans modelov za oceno dolžine koraka

Adaptive Inertial Sensor-Based Step Length Estimation Model

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches

Adaptive Inertial Sensor-Based Step Length Estimation Model

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches

Inertial sensor-based step length estimation model by means of principal component analysis

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements