MORTAL: A Tool of Automatically Designing Relational Storage Schemas for Multi-model Data through Reinforcement Learning

Considering relational databases having powerful capabilities in handling security, user authentication, query optimization, etc., several commercial and academic frameworks reuse relational databases to store and query semi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, property graph). However, these works concentrate on managing one of the above data models with RDBMSs. That is, it does not exploit the underlying tools to automatically generate the relational schema for storing multi-model data. In this demonstration, we present a novel reinforcement learning-based tool called MORTAL. Specifically, given multi-model data containing different data models and a set of queries, it could automatically design a relational schema to store these data while having a great query performance. To demonstrate it clearly, we are centered around the following modules: generating initial state based on loaded multi-model data, influencing learning process by setting parameters, controlling generated relational schema through providing semantic constraints, improving the query performance of relational schema by specifying queries, and a highly interactive interface for showing query performance and storage consumption when users adjust the generated relational schema.Peer reviewe

Keyword Searches and Schema Transformation for Multi-Model Databases

The Variety of data is promoting the evolution and development of databases. One of the influence results is the emergence of multi-model databases. So far, the database community has proposed quite a few multi-model databases to support different data models, but these databases adopt diverse methods to implement their data storage and query, which results in a heavy burden for novices to use multi-model databases. Considering this, we present our first research topic - how to employ the keyword searches method as an alternative way to explore and query multi-model databases. Besides, compared with the mature and robust relational databases dominating the current market, multi-model databases - which can not yet match them in transaction management, query optimization, security, etc. - still need time to perfect their foundations of the mathematic theory and boost performance. Considering this, we present our second research topic - how to use relational databases as an alternative way to store and query well-structured data and NoSQL data uniformly. For the first research problem, we utilize the probabilistic formalism of quantum physics to bring the problem into vector spaces and exploit non-classical probabilities to find top-k the most relevant results. As for the second research topic, it requires designing a good relational schema to store these various data in relational databases. But the challenge is that we need to address the difference of structure between flat relational tables and complex multi-model data. To address this problem, we review all relevant works, analyze existing methods, and give a literature review. As a result, we find these works focusing on handling one single data model by relational databases. There is no relevant research to handle multi-model data. Against this challenge, we prepare to employ the reinforcement learning method. This is because this method could automatically obtain an excellent relational schema from the given multi-model data and queries by interacting with the outer environment. To make this idea work in the field of databases, we define the input, goal, reward, policy, and observation according to our purpose, respectively. Besides, we present a Double Q-tables algorithm to assist in decreasing the complexity of the learning process.Datan monimuotoisuus edistää tietokantojen kehittymistä. Eräs vaikuttavimmista kehityskuluista on monimallisten tietokantojen synty,. Tietokantayhteisö on kehittänyt useita monimallisia tietokantoja tukemaan erilaisia tietomalleja. Näissä monimallisissa tietokannoissa on toteutettu monipuolisesti erilaisia tapoja tallentaa dataa ja suorittaa tietokantakyselyjä, mikä toisaalta aiheuttaa aloittelijoille vaikeuksia monimallisten tietokantojen käyttämisessä. Aloittelijoiden omaksuttava jokaisen monimallisen tietokannan kyselykieli erikseen. Näiden lisäksi käyttäjien täytyy hallita monimutkaisia ja dynaamisesti kehittyviä tietokantakaavioita, jotta he voivat muodostaa kyselyitä monimallisissa tietokannoissa. Ottaen huomioon nämä haasteet esitämme ensimmäisen tutkimuskysymyksen: kuinka käyttää avainsanahakua vaihtoehtoisena tapana suorittaa kyselyitä monimallisissa tietokannoissa? Ensimmäisen tutkimuskysymyksen osalta hyödynnämme kvanttifysiikkaan liittyvää todennäköisyyslaskennan formalismia, joka muotoilee ongelman vektoriavaruuksien avulla ja hyödyntää ei-klassisia todennäköisyyksiä. Tällöin löydetään k olennaisinta tulosta, jotka koostuvat useasta komponentista ja tietomallista. Lähestymme toista tutkimusongelmaa havaitsemalla, että monimallisen tiedon tallentaminen relaatiotietokantaan vaatii hyvän relaatiotietokantakaavion kehittämistä. Meidän täytyy ottaa huomioon yksiulotteisten relaatioiden ja monimallisen tiedon rakenteelliset erot. Aloitamme katsauksella nykyiseen aiheeseen liittyvään tutkimukseen, analysoimme olemassa olevia menetelmiä sekä kokoamme kirjallisuuskatsauksen aiheesta. Selvityksen perusteella voimme havaita, että nämä tutkimukset keskittyvät yhden tietomallin käsittelemiseen relaatiotietokannoissa eikä monimallista tietoa ole toistaiseksi käsitelty tutkimuksissa lainkaan. Vastataksemme tähän haasteeseen kehitämme vahvistusoppimiseen perustuvan menetelmän, jolla pystymme tuottamaan erinomaisen relaatiokaavion monimalliselle tiedolle sekä kyselyille vuorovaikutuksessa ympäristön kanssa. Jotta kykenemme soveltamaan tätä ideaa tietokantatutkimuksessa, määrittelemme tarkoituksiimme sopivan syötteen, tavoitteen, palkkiosysteemin, menettelytavan ja havainnot. Lisäksi esittelemme ns. Double Q-tables -algoritmin, joka auttaa koneoppimisprosessin vaativuuden vähentämisessä

Quantum-Inspired Keyword Search on Multi-model Databases

With the rising applications implemented in different domains, it is inevitable to require databases to adopt corresponding appropriate data models to store and exchange data derived from various sources. To handle these data models in a single platform, the community of databases introduces a multi-model database. And many vendors are improving their products from supporting a single data model to being multi-model databases. Although this brings benefits, spending lots of enthusiasm to master one of the multi-model query languages for exploring a database is unfriendly to most users. Therefore, we study using keyword searches as an alternative way to explore and query multi-model databases. In this paper, we attempt to utilize quantum physics's probabilistic formalism to bring the problem into vector spaces and represent events (e.g., words) as subspaces. Then we employ a density matrix to encapsulate all the information over these subspaces and use density matrices to measure the divergence between query and candidate answers for finding top-k the most relevant results. In this process, we propose using pattern mining to identify compounds for improving accuracy and using dimensionality reduction for reducing complexity. Finally, empirical experiments demonstrate the performance superiority of our approaches over the state-of-the-art approaches.Peer reviewe

Diabetes impairs fracture healing through Foxo1 mediated disruption of ciliogenesis

Abstract Foxo1 upregulation is linked to defective fracture healing under diabetic conditions. Previous studies demonstrated that diabetes upregulates Foxo1 expression and activation and diabetes impairs ciliogenesis resulting in defective fracture repair. However, the mechanism by which diabetes causes cilia loss during fracture healing remains elusive. We report here that streptozotocin (STZ)-induced type 1 diabetes mellitus (T1DM) dramatically increased Foxo1 expression in femoral fracture calluses, which thereby caused a significant decrease in the expression of IFT80 and primary cilia number. Ablation of Foxo1 in osteoblasts in OSX cretTA Foxo1 f/f mice rescued IFT80 expression and ciliogenesis and restored bone formation and mechanical strength in diabetic fracture calluses. In vitro, advanced glycation end products (AGEs) impaired cilia formation in osteoblasts and reduced the production of a mineralizing matrix, which were rescued by Foxo1 deletion. Mechanistically, AGEs increased Foxo1 expression and transcriptional activity to inhibit IFT80 expression causing impaired cilia formation. Thus, our findings demonstrate that diabetes impairs fracture healing through Foxo1 mediated inhibition of ciliary IFT80 expression and primary cilia formation, resulting in impaired osteogenesis. Inhibition of Foxo1 and/or restoration of cilia formation has the potential to promote diabetes-impaired fracture healing

Room temperature and humidity decoupling control of common variable air volume air-conditioning system based on bilinear characteristics

Variable air volume (VAV) air-conditioning (AC) systems are widely employed to achieve a comfortable room thermal and humid environment depending on its better regulation performance and energy efficiency. In the single coil VAV AC system, conventional proportional-integral (PI) control algorithm is usually adopted to track the set-points of the room temperature and humidity by regulating the supply air flow rate and the chilled water flow rate, respectively. However, the control performance is usually not good due to the high coupling of the heat and mass transfer in the air-handling unit (AHU). A model-based control method is developed to realize the decoupling control of the room temperature and humidity according to the bilinear characteristics of the temperature and humidity variation. In this control method, a bilinear room temperature controller is used to track the room temperature set-point based on the real-time cooling load, while a room humidity controller is used to track the room humidity set-point depending on the real-time humidity load. The control performance was validated in a simulated VAV AC system. The test results show that comparing with the conventional PI control, the room temperature and humidity are controlled much more robustly and accurately by using the proposed model-based control method

The Effect of IFT80 Deficiency in Osteocytes on Orthodontic Loading-Induced and Physiologic Bone Remodeling: In Vivo Study

Osteocytes are the main mechanosensory cells during orthodontic and physiologic bone remodeling. However, the question of how osteocytes transmit mechanical stimuli to biological responses remains largely unanswered. Intraflagellar transport (IFT) proteins are important for the formation and function of cilia, which are proposed to be mechanical sensors in osteocytes. In particular, IFT80 is highly expressed in mouse skulls and essential for ciliogenesis. This study aims to investigate the short- and long-term effects of IFT80 deletion in osteocytes on orthodontic bone remodeling and physiological bone remodeling in response to masticatory force. We examined 10-week-old experimental DMP1 CRE+.IFT80f/f and littermate control DMP1 CRE−.IFT80f/f mice. After 5 and 12 days of orthodontic force loading, the orthodontic tooth movement distance and bone parameters were evaluated using microCT. Osteoclast formation was assessed using TRAP-stained paraffin sections. The expression of sclerostin and RANKL was examined using immunofluorescence stain. We found that the deletion of IFT80 in osteocytes did not significantly impact either orthodontic or physiologic bone remodeling, as demonstrated by similar OTM distances, osteoclast numbers, bone volume fractions (bone volume/total volume), bone mineral densities, and the expressions of sclerostin and RANKL. Our findings suggest that there are other possible mechanosensory systems in osteocytes and anatomic limitations to cilia deflection in osteocytes in vivo