Si l’immobilier est de loin la plus importante classe d’actifs de notre économie, elle est également l’une des dernières à ne pas disposer d’un marché de dérivés mature. Des études académiques récentes ont montré que le manque de compréhension de leurs prix en est la principale raison. Ce travail doctoral cherche à y remédier. Par la conduite d’études à la fois théoriques et empiriques, nous sommes parvenus à déterminer leurs caractéristiques statistiques, leurs facteurs de risque mais aussi à appréhender l’intérêt de ces produits en terme de fonction de découverte des prix. Si les dérivés immobiliers constituent un outil de paramétrisation du risque immobilier essentiel, ils offrent également la possibilité aux investisseurs comme aux pouvoirs publics de disposer d’informations qui ne seraient pas disponibles autrement / Despite the fact that real estate is the largest asset class in our economy, it is one of the few that do not have a mature derivatives market. Recent academic studies have shown that the lack of understanding of real estate derivatives’ prices is the main reason for the absence of a market. This dissertation aims to change this. By conducting theoretical and empirical studies we describe their statistical characteristics, their risk factors, and we highlight their importance in terms of price discovery function. Property derivatives are an essential tool for risk management, but they also offer for investors and regulators a source of information that would otherwise not be available

In this thesis, we developed SemKPSearch which is a tool for searching documents by the keyphrases that are semantically related with the given query phrase. By relating the keyphrases semantically, we aim to provide users an extended search and browsing capability over a document collection and to increase the number of related results returned for a keyphrase query. Keyphrases provide a brief summary of the content of documents. They can be either author assigned or automatically extracted from the documents. SemKPSearch uses SemKPIndexes which are generated with the keyphrases of the documents. SemKPIndex is a keyphrase index extended with a keyphrase to keyphrase index which stores the semantic relation score between the keyphrases in the document collection. Semantic relation score between keyphrases is calculated using a metric which considers the similarity score between words of the keyphrases. The semantic similarity score between two words is determined with the help of two word-to-word semantic similarity metrics, namely the metric of Wu&amp / Palmer and the metric of Li et al. SemKPSearch is evaluated by the human evaluators which are all computer engineers. For the evaluation, in addition to the author assigned keyphrases, the keyphrases automatically extracted by employing the state-of-the-art algorithm KEA are used to create keyphrase indexes.

<p dir="ltr">In recent years, massive amounts of data have been generated from various types of devices or services. For these data, update-intensive workloads where the data update their status periodically and continuously are common. The Log-Structured-Merge (LSM, for short) is a widely-used indexing technique in various systems, where index structures buffer insert operations into the memory layer and flush them into disk when the data size in memory exceeds a threshold. Despite its noble ability to handle write-intensive (i.e., insert-intensive) workloads, LSM suffers from degraded query performance due to its inefficiency on index maintenance of secondary keys to handle update-intensive workloads.</p><p dir="ltr">This dissertation focuses on the efficient support of update-intensive workloads for LSM-based indexes. First, the focus is on the optimization of LSM secondary-key indexes and their support for update-intensive workloads. A mechanism to enable the LSM R-tree to handle update-intensive workloads efficiently is introduced. The new LSM indexing structure is termed the LSM RUM-tree, an LSM R-tree with Update Memo. The key insights are to reduce the maintenance cost of the LSM R-tree by leveraging an additional in-memory memo structure to control the size of the memo to fit in memory. In the experiments, the LSM RUM-tree achieves up to 9.6x speedup on update operations and up to 2400x speedup on query operations.</p><p dir="ltr">Second, the focus is to offer several significant advancements in the context of the LSM RUM-tree. We provide an extended examination of LSM-aware Update Memo (UM) cleaning strategies, elucidating how effectively each strategy reduces UM size and contributes to performance enhancements. Moreover, in recognition of the imperative need to facilitate concurrent activities within the LSM RUM-Tree, particularly in multi-threaded/multi-core environments, we introduce a pivotal feature of concurrency control for the update memo. The novel atomic operation known as Compare and If Less than Swap (CILS) is introduced to enable seamless concurrent operations on the Update Memo. Experimental results attest to a notable 4.5x improvement in the speed of concurrent update operations when compared to existing and baseline implementations.</p><p dir="ltr">Finally, we present a novel technique designed to improve query processing performance and optimize storage management in any secondary LSM tree. Our proposed approach introduces a new framework and mechanisms aimed at addressing the specific challenges associated with secondary indexing in the structure of the LSM tree, especially in the context of secondary LSM B+-tree (LSM BUM-tree). Experimental results show that the LSM BUM-tree achieves up to 5.1x speedup on update-intensive workloads and 107x speedup on update and query mixed workloads over existing LSM B+-tree implementations.</p>

Background: The development of an objective and comprehensive drug-based index of physical function for older adults has the potential to more accurately predict fall risk. Design: the index was developed using 862 adults (ages 57–85) from the National Social Life, Health, and Aging Project (NSHAP) Wave 1 study. The index was evaluated in 70 adults (ages 51–88) from a rehabilitation study of dizziness and balance. Methods: The prevalence among 601 drugs for 1,694 side effects was used with fall history to determine the magnitude of each side effect's contribution towards physical function. This information was used to calculate a Medication-based Index of Physical function (MedIP) score for each individual based on his or her medication profile. The MedIP was compared to the timed up and go (TUG) test as well as drug counts using receiver operating characteristic (ROC) analysis. The associations between various indices of physical function and MedIP were calculated. Results: Within the NSHAP data set, the MedIP was better than drug counts or TUG at predicting falls based on ROC analysis. Using scores above and below the cutpoint, the MedIP was a significant predictor of falls (OR = 2.61 [95% CI 1.83, 3.64]; P < 0.001). Using an external data set, it was shown that the MedIP was significantly correlated with fall number (P = 0.044), composite physical function (P = 0.026) and preferred gait speed (P = 0.043).