Exploiting Intrinsic Clustering Structure in Discrete-Valued Data Sets for Efficient Knowledge Discovery in the Presence of Missing Data

Scalable algorithm design has become central in the era of large-scale data analysis. The vast amounts of data pouring in from a diverse set of application domains, such as bioinformatics, recommender systems, sensor systems, and social networks, cannot be analyzed efficiently using many data mining and statistical tools that were designed for a small scale setting. It is an ongoing challenge to the data mining, machine learning, and statistics communities to design new methods for efficient data analysis. Confounding this challenge is the noisy and incomplete nature of real-world data sets. Research scientists as well as practitioners in industry need to find meaningful patterns in data with missing value rates often as high as 99%, in addition to errors in the data that can obstruct accurate analyses. My contribution to this line of research is the design of new algorithms for scalable clustering, data reduction, and similarity evaluation by exploiting inherent clustering structure in the input data to overcome the challenges of significant amounts of missing entries. I demonstrate that, by focusing on underlying clustering properties of the data, we can improve the efficiency of several data analysis methods on sparse, discrete-valued data sets. I highlight new methods that I have developed with my collaborators for three diverse knowledge discovery tasks: (1) clustering genetic markers into linkage groups, (2) reducing large-scale genetic data to a much smaller, more accurate representative data set, and (3) computing similarity between users in recommender systems. In each case, I point out how the underlying clustering structure can be used to design more efficient algorithms, even when high missing value rates are present

Vector representation of Internet domain names using Word embedding techniques

Word embeddings is a well-known set of techniques widely used in natural language processing ( NLP ). This thesis explores the use of word embeddings in a new scenario. A vector space model ( VSM) for Internet domain names ( DNS) is created by taking core ideas from NLP techniques and applying them to real anonymized DNS log queries from a large Internet Service Provider ( ISP) . The main goal is to find semantically similar domains only using information of DNS queries without any other knowledge about the content of those domains. A set of transformations through a detailed preprocessing pipeline with eight specific steps is defined to move the original problem to a problem in the NLP field. Once the preprocessing pipeline is applied and the DNS log files are transformed to a standard text corpus, we show that state-of-the-art techniques for word embeddings can be successfully applied in order to build what we called a DNS-VSM (a vector space model for Internet domain names). Different word embeddings techniques are evaluated in this work: Word2Vec (with Skip-Gram and CBOW architectures), App2Vec (with a CBOW architecture and adding time gaps between DNS queries), and FastText (which includes sub-word information). The obtained results are compared using various metrics from Information Retrieval theory and the quality of the learned vectors is validated with a third party source, namely, similar sites service offered by Alexa Internet, Inc2 . Due to intrinsic characteristics of domain names, we found that FastText is the best option for building a vector space model for DNS. Furthermore, its performance (considering the top 3 most similar learned vectors to each domain) is compared against two baseline methods: Random Guessing (returning randomly any domain name from the dataset) and Zero Rule (returning always the same most popular domains), outperforming both of them considerably. The results presented in this work can be useful in many engineering activities, with practical application in many areas. Some examples include websites recommendations based on similar sites, competitive analysis, identification of fraudulent or risky sites, parental-control systems, UX improvements (based on recommendations, spell correction, etc.), click-stream analysis, representation and clustering of users navigation profiles, optimization of cache systems in recursive DNS resolvers (among others). Finally, as a contribution to the research community a set of vectors of the DNS-VSM trained on a similar dataset to the one used in this thesis is released and made available for download through the github page in [1]. With this we hope that further work and research can be done using these vectors.La vectorización de palabras es un conjunto de técnicas bien conocidas y ampliamente usadas en el procesamiento del lenguaje natural ( PLN ). Esta tesis explora el uso de vectorización de palabras en un nuevo escenario. Un modelo de espacio vectorial ( VSM) para nombres de dominios de Internet ( DNS ) es creado tomando ideas fundamentales de PLN, l as cuales son aplicadas a consultas reales anonimizadas de logs de DNS de un gran proveedor de servicios de Internet ( ISP) . El objetivo principal es encontrar dominios relacionados semánticamente solamente usando información de consultas DNS sin ningún otro conocimiento sobre el contenido de esos dominios. Un conjunto de transformaciones a través de un detallado pipeline de preprocesamiento con ocho pasos específicos es definido para llevar el problema original a un problema en el campo de PLN. Una vez aplicado el pipeline de preprocesamiento y los logs de DNS son transformados a un corpus de texto estándar, se muestra que es posible utilizar con éxito técnicas del estado del arte respecto a vectorización de palabras para construir lo que denominamos un DNS-VSM (un modelo de espacio vectorial para nombres de dominio de Internet). Diferentes técnicas de vectorización de palabras son evaluadas en este trabajo: Word2Vec (con arquitectura Skip-Gram y CBOW) , App2Vec (con arquitectura CBOW y agregando intervalos de tiempo entre consultas DNS ), y FastText (incluyendo información a nivel de sub-palabra). Los resultados obtenidos se comparan usando varias métricas de la teoría de Recuperación de Información y la calidad de los vectores aprendidos es validada por una fuente externa, un servicio para obtener sitios similares ofrecido por Alexa Internet, Inc . Debido a características intrínsecas de los nombres de dominio, encontramos que FastText es la mejor opción para construir un modelo de espacio vectorial para DNS . Además, su performance es comparada contra dos métodos de línea base: Random Guessing (devolviendo cualquier nombre de dominio del dataset de forma aleatoria) y Zero Rule (devolviendo siempre los mismos dominios más populares), superando a ambos de manera considerable. Los resultados presentados en este trabajo pueden ser útiles en muchas actividades de ingeniería, con aplicación práctica en muchas áreas. Algunos ejemplos incluyen recomendaciones de sitios web, análisis competitivo, identificación de sitios riesgosos o fraudulentos, sistemas de control parental, mejoras de UX (basada en recomendaciones, corrección ortográfica, etc.), análisis de flujo de clics, representación y clustering de perfiles de navegación de usuarios, optimización de sistemas de cache en resolutores de DNS recursivos (entre otros). Por último, como contribución a la comunidad académica, un conjunto de vectores del DNS-VSM entrenado sobre un juego de datos similar al utilizado en esta tesis es liberado y hecho disponible para descarga a través de la página github en [1]. Con esto esperamos a que más trabajos e investigaciones puedan realizarse usando estos vectores

DR-Advisor: A Data-Driven Demand Response Recommender System

Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR ap- proaches are predominantly completely manual and rule-based or involve deriving first principles based models which are ex- tremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. The challenge is in evaluating and taking control decisions at fast time scales in order to curtail the power consumption of the building, in return for a financial reward. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW and over $45, 000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager and provides suitable control actions to meet the desired load curtailment while main- taining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% prediction accuracy for 8 buildings on Penn’s campus. We compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy prediction

Big Data mining and machine learning techniques applied to real world scenarios

Data mining techniques allow the extraction of valuable information from heterogeneous and possibly very large data sources, which can be either structured or unstructured. Unstructured data, such as text files, social media, mobile data, are much more than structured data, and grow at a higher rate. Their high volume and the inherent ambiguity of natural language make unstructured data very hard to process and analyze. Appropriate text representations are therefore required in order to capture word semantics as well as to preserve statistical information, e.g. word counts. In Big Data scenarios, scalability is also a primary requirement. Data mining and machine learning approaches should take advantage of large-scale data, exploiting abundant information and avoiding the curse of dimensionality. The goal of this thesis is to enhance text understanding in the analysis of big data sets, introducing novel techniques that can be employed for the solution of real world problems. The presented Markov methods temporarily achieved the state-of-the-art on well-known Amazon reviews corpora for cross-domain sentiment analysis, before being outperformed by deep approaches in the analysis of large data sets. A noise detection method for the identification of relevant tweets leads to 88.9% accuracy in the Dow Jones Industrial Average daily prediction, which is the best result in literature based on social networks. Dimensionality reduction approaches are used in combination with LinkedIn users' skills to perform job recommendation. A framework based on deep learning and Markov Decision Process is designed with the purpose of modeling job transitions and recommending pathways towards a given career goal. Finally, parallel primitives for vendor-agnostic implementation of Big Data mining algorithms are introduced to foster multi-platform deployment, code reuse and optimization

Choosing reputable resources in unstructured peer-to-peer networks using trust overlays

In recent years Peer-to-Peer Systems have gained popularity, and are best known as a convenient way of sharing content. However, even though they have existed for a considerable length of time, no method has yet been developed to measure the quality of the service they provide nor to identify cases of misbehaviour by individual peers. This thesis attempts to give to P2P systems some quality measures with the potential of giving querying peers criteria by which to judge and make predictions about the behaviour of their counterparts. The work includes the design of a reputation system from which querying peers can seek guidance before they commit to transaction with another peer. but usually as Reputation and Recommender systems have existed for years centralized services. Our innovation is the use of a distributed recommendation system which will be supported by the peers themselves. The system operates in the same manner as "word-of-mouth" in human societies does. In contrast to other reputation systems the word-of-mouth technique is itself decentralized since there is no need for central entities to exist as long as there are participants willing to be involved in the recommendation process. In order for a society to exist it is necessary that members have some way of knowing each other so that they can form relationships. The main element used to link members in an online community together is a virtual trust relationship that can be identified from the evidence that exists about their virtual partnerships. In our work we approximate the level of trust that could exist between any two parties by exploiting their similarity, constructing a network that is known as "web of trust". Using the transitivity property of trust, we make it possible for more peers to come in to contact through virtual trust relationships and thus get better results than in an ordinary system.EThOS - Electronic Theses Online ServiceGreek State Scholarships FoundationGBUnited Kingdo