We provide a comprehensive overview of current approaches and systems for combining graphs and time series data. We categorize existing systems into four architectural categories and analyze how these systems meet different requirements and exhibit distinct implementation characteristics to support both data types in a unified manner. Our overview aims to help readers understand and evaluate current options and trade-offs, such as the degree of cross-model integration, maturity, and openness.
Miika Hannula, Minna Hirvonen, Juha Kontinen
et al.
We initiate an investigation how the fundamental concept of independence can be represented effectively in the presence of incomplete information in relational databases. The concepts of possible and certain independence are proposed, and first results regarding the axiomatisability and computational complexity of implication problems associated with these concepts are established. In addition, several results for the data and the combined complexity of model checking are presented. The findings help reduce computational overheads associated with the processing of updates and answering of queries.
We describe and analyze different approaches to represent ordinal patterns. All of these can be found in the literature. The most important representations (plus sub-classes) are compared in terms of their applicability from different angles. Namely we consider digital implementation, inverse patterns and ties between values. At the end we provide a guideline on which occasions which representation should be used.
This report evaluates the efficiency of Graph Edit Distance (GED) computation for graph similarity search, comparing Cascading Metric Trees (CMT) with brute-force verification. Despite the anticipated advantages of CMT, our findings indicate it does not consistently outperform brute-force methods in speed. The study, based on graph data from PubChem, suggests that the computational complexity of GED-based GSS remains a challenge.
The R arules package implements a comprehensive infrastructure for representing, manipulating, and analyzing transaction data and patterns using frequent itemsets and association rules. The package also provides a wide range of interest measures and mining algorithms, including the code of Christian Borgelt's popular and efficient C implementations of the association mining algorithms Apriori and Eclat, and optimized C/C++ code for mining and manipulating association rules using sparse matrix representation. This document describes the new Python package arulespy, which makes this infrastructure available for Python users.
The most common archetypes to identify relevant information in large datasets and find the bestoptions according to some preferences or user criteria, are the top-k queries (ranking method based ona score function defined over the records attributes) and skyline queries (based on Pareto dominance oftuples). Despite their large diffusion, both approaches have their pros and cons. In this survey paper, a comparison is made between these methods and the Flexible Skylines, which is a framework that combines the ranking and skyline approaches using the novel concept ofF-dominanceto a set of monotone scoring function F.
The datasets available nowadays are very rich and complex, but how do we reach the information we are looking for? In this survey, two different approaches to query a dataset are analyzed and algorithms for each type are explained. Specifically, the TA and NRA have been analyzed for the Top-K query and the Basic Block Nested Loops has been examined for the skyline query. Moreover, it's explained the core idea behind the Prioritized and Flexible skyline. In the end, the pros and cons of each type of analyzed query have been evaluated based on different criteria.
The scalability of process mining techniques is one of the main challenges to tackling the massive amount of event data produced every day in enterprise information systems. To this purpose, filtering and sampling techniques are proposed to keep a subset of the behavior of the original log and make the application of process mining techniques feasible. While techniques for filtering/sampling traditional event logs have been already proposed, filtering/sampling object-centric event logs is more challenging as the number of factors (events, objects, object types) to consider is significantly higher. This paper provides some techniques to filter/sample object-centric event logs.
The paper presents the results of the experiments that were conducted to confirm the main ideas of the proposed approach to determining the objects innovativeness. This approach assumed that the product life cycle of whose descriptions are placed in different data warehouses is adequate. The proposed formal model allows us to calculate the quantitative value of the additive evaluation criterion of objects innovativeness. The obtained experimental data make it possible to evaluate the adopted approach correctness.
Abstract Type 2 diabetes is currently experiencing an outbreak worldwide. GLP-1 effectively lowers blood glucose level as an emerging target for the treatment of type 2 diabetes. However, the application of GLP-1 is limited by short half-life and too expensive cost in clinic. In this study we employed the food-grade probiotics as delivery system to express human GLP-1 and its analogue. Recombinant lactococcus lactis could express GLP-1 and analogue in vitro and modified GLP-1 analogue was more resistant to DPP-4 degradation. Oral administration of GLP-1 analogue could reduce the fat mass. More importantly, GLP-1 analogue improved hyperglycemia and insulin resistance in Db/Db mouse although the insulin secretion is not observed in vitro. Our study demonstrates that lactococcus lactis genetically modified with single amino acid mutation could prolong half-life of GLP-1 and increase insulin sensitivity in Db/Db mouse model as an oral drug delivery system driving the development and innovation of drug therapy for type 2 diabetes.
Rashad Rasool Khan, Thuwaini Hashil Abdullah Al-Ghafri, Salim Ali Humaid Al-Khatri
et al.
AbstractEnvironmental pollution, ill-effects on human health, insecticide resistance development and insect pest resurgence are some serious problems that may arise due to excessive chemical spraying for pest control. Despite of heavy aerial and surface insecticide spraying, incomplete control of Ommatissus lybicus de Bergevin 1930 (Homoptera: Tropiduchidae) is reported in Oman every year, which requires investigation of insecticides resistance in pest. Fifteen populations of O. lybicus, collected from diverse vicinities were exposed along with a deltamethrin-selected (DEL-SEL) and lab-susceptible (LAB-SUS) strain to deltamethrin and fenitrothion insecticides in bioassay tests for estimation of their resistance status. All the field populations of O. lybicus, exhibited minor (RR = 3–5-folds) to low (RR = 5–10-folds) levels of resistance to deltamethrin, however, two out fifteen populations collected from Al-Hajir and Sint were found susceptible against fenitrothion (RR < 3-folds). Enzyme assays were conducted to detect the activities of cytochrome p-450-reductase (CPR), glutathione s-transferase (GST) and acetylcholinesterase (AChE) in the field collected, DEL-SEL and LAB-SUS strains of O. lybicus. Results revealed significantly increased activities of all enzymes in the field collected as well as DEL-SEL strains of O. lybicus when compared with LAB-SUS strains.
Association rules express implication formed relations among attributes in databases of itemsets. The apriori algorithm is presented, the basis for most association rule mining algorithms. It works by pruning away rules that need not be evaluated based on the user specified minimum support confidence. Additionally, variations of the algorithm are presented that enable it to handle quantitative attributes and to extract rules about generalizations of items, but preserve the downward closure property that enables pruning. Intertransformation of the extensions is proposed for special cases.
A significant category of NoSQL approaches is known as graph da-tabases. They are usually represented by one property graph. We introduce a functional approach to modelling relations and property graphs. Single-valued and multivalued functions will be sufficient in this case. Then, a typed λ-calculus, i.e., the language of lambda terms, will be used as a data manipulation lan-guage. Some integration options at the query language level are discussed.
Gaetan de Rassenfosse, Martin Kracker, Gianluca Tarasconi
This paper provides a technical introduction to the PATSTAT Register database, which contains bibliographical, procedural and legal status data on patent applications handled by the European Patent Office. It presents eight MySQL queries that cover some of the most relevant aspects of the database for research purposes. It targets academic researchers and practitioners who are familiar with the PATSTAT database and the MySQL language.
In this paper, we first define a logical theory representing an XML database supporting XPath as query language and XUpdate as modification language. We then extend our theory with predicates allowing us to specify the security policy protecting the database. The security policy includes rules addressing the read and write privileges. We propose axioms to derive the database view each user is permitted to see. We also propose axioms to derive the new database content after an update.
This paper presents a family of algorithms for fast subset filtering within ordered sets of integers representing composite keys. Applications include significant acceleration of (ad-hoc) analytic queries against a data warehouse without any additional indexing. The algorithms work for point, range and set restrictions on multiple attributes, in any combination, and are inherently multidimensional. The main idea consists in intelligent combination of sequential crawling with jumps over large portions of irrelevant keys. The way to combine them is adaptive to characteristics of the underlying data store.