Modern application domains such as Composite Event Recognition (CER) and real-time Analytics require the ability to dynamically refresh query results under high update rates. Traditional approaches to this problem are based either on the materialization of subresults (to avoid their recomputation) or on the recomputation of subresults (to avoid the space overhead of materialization). Both techniques have recently been shown suboptimal: instead of materializing results and subresults, one can maintain a data structure that supports efficient maintenance under updates and can quickly enumerate the full query output, as well as the changes produced under single updates. Unfortunately, these data structures have been developed only for aggregate-join queries composed of equi-joins, limiting their applicability in domains such as CER where temporal joins are commonplace. In this paper, we present a new approach for dynamically evaluating queries with multi-way θ-joins under updates that is effective in avoiding both materialization and recomputation of results, while supporting a wide range of applications. To do this we generalize Dynamic Yannakakis, an algorithm for dynamically processing acyclic equi-join queries. In tandem, and of independent interest, we generalize the notions of acyclicity and free-connexity to arbitrary θ-joins. We instantiate our framework to the case where θ-joins are only composed of equalities and inequalities (<, ≤, =, >, ≥) and experimentally compare this algorithm, called IEDyn, to state of the art CER systems as well as incremental view maintenance engines. IEDyn performs consistently better than the competitor systems with up to two orders of magnitude improvements in both time and memory consumption.

We demonstrate TimeTravel, an efficient DBMS system for seamless integrated querying of past and (forecasted) future values of time series, allowing the user to view past and future values as one joint time series. This functionality is important for advanced application domain like energy. The main idea is to compactly represent time series as models. By using models, the TimeTravel system answers queries approximately on past and future data with error guarantees (absolute error and confidence) one order of magnitude faster than when accessing the time series directly. In addition, it efficiently supports exact historical queries by only accessing relevant portions of the time series. This is unlike existing approaches, which access the entire time series to exactly answer the query. To realize this system, we propose a novel hierarchical model index structure. As real-world time series usually exhibits seasonal behavior, models in this index incorporate seasonality. To construct a hierarchical model index, the user specifies seasonality period, error guarantees levels, and a statistical forecast method. As time proceeds, the system incrementally updates the index and utilizes it to answer approximate and exact queries. TimeTravel is implemented into PostgreSQL, thus achieving complete user transparency at the query level. In the demo, we show the easy building of a hierarchical model index for a real-world time series and the effect of varying the error guarantees on the speed up of approximate and exact queries.

Dans ce travail de recherche, nous sommes intéresses par la modélisation de l'hématopoïèse. Les cellules souches hématopoïétiques (CSH) sont des cellules indifférenciées de la moelle osseuse, possédant la capacité de se renouveler et de se différencier (pour la production des globules rouges, globules blancs et les plaquettes). Le processus de l'hématopoïèse souvent révèle des irrégularités qui causent les maladies hématologiques. En modélisant la leucémie myéloide chronique (LMC), une maladie hématologique fréquente, nous représentons l'hématopoïèse des cellules normales et cancéreuses par un système d'équations différentielles ordinaires (EDO). L'homéostasie des cellules normales et différente de l'homéostasie des cellules cancéreuses, et dépend de quelques lignées des cellules normales et cancéreuses. Nous analysons la dynamique globale du modèle pour obtenir les conditions de régénération de l'hématopoïèse ou bien la persistance de la LMC. Nous démontrons aussi que la coexistence des cellules normales et cancéreuses ne peut avoir lieu pour longtemps. Imatinib est un traitement de base de la LMC, avec un dosage variant de 400 à 1000 mg par jour. Certains patients présentent des réponses différentes à la thérapie, pouvant être hématologique, cytogénétique et moléculaire. La thérapie échoue dans deux cas: le patient demande un temps plus long pour réagir, alors il s'agit d'une réponse suboptimale; ou bien le patient résiste après une bonne réponse initiale. Pour déterminer le dosage optimal, nécessaire à la réduction des cellules cancéreuses, nous représentons les effets de la thérapie par un problème de contrôle optimal. Notre but est de minimiser le cout du traitement et le nombre des cellules cancéreuses. La réponse suboptimale, la résistance et le rétablissement sont alors obtenus suivant l'influence de l'imatinib sur les taux de division et de mortalité des cellules cancéreuses. Nous étudions par ailleurs l'hématopoïèse selon un modèle structuré en age, décrivant l'évolution des CSH normales et cancéreuses. Nous démontrons que le taux de division des CSH cancéreuses joue un rôle important dans la détermination du contrôle optimal. En contrôlant la croissance des cellules normales et cancéreuses avec compétition inter spécifique, nous démontrons que le dosage optimal dépend de l'homéostasie des CSH cancéreuses. / Modelling hematopoiesis represents a feature of our research. Hematopoietic stem cells (HSC) are undifferentiated cells, located in bone marrow, with unique abilities of self-renewal and differentiation (production of white cells, red blood cells and platelets).The process of hematopoiesis often exhibits abnormalities causing hematological diseases. In modelling Chronic Myeloid Leukemia (CML), a frequent hematological disease, we represent hematopoiesis of normal and leukemic cells by means of ordinary differential equations (ODE). Homeostasis of normal and leukemic cells are supposed to be different and depend on some lines of normal and leukemic HSC. We analyze the global dynamics of the model to obtain the conditions for regeneration of hematopoiesis and persistence of CML. We prove as well that normal and leukemic cells can not coexist for a long time. Imatinib is the main treatment of CML, with posology varying from 400 to 1000 mg per day. Some affected individuals respond to therapy with various levels being hematologic, cytogenetic and molecular. Therapy fails in two cases: the patient takes a long time to react, then suboptimal response occurs; or the patient resists after an initial response. Determining the optimal dosage required to reduce leukemic cells is another challenge. We approach therapy effects as an optimal control problem to minimize the cost of treatment and the level of leukemic cells. Suboptimal response, resistance and recovery forms are obtained through the influence of imatinib onto the division and mortality rates of leukemic cells. Hematopoiesis can be investigated according to age of cells. An age-structured system, describing the evolution of normal and leukemic HSC shows that the division rate of leukemic HSC plays a crucial role when determining the optimal control. When controlling the growth of cells under interspecific competition within normal and leukemic HSC, we prove that optimal dosage is related to homeostasis of leukemic HSC.