SCSA: Evaluating skyline queries in incomplete data

Skyline queries have been extensively incorporated in various contemporary database applications. The list includes but is not limited to multi-criteria decision-making systems, decision support systems, and recommendation systems. Due to its great benefits and wide application range, many skyline algorithms have already been proposed in numerous data settings. Nonetheless, most researchers presume the completion of data meaning that all data item values are available. Since this assumption cannot be sustained in a large number of real-world database applications, the existing algorithms are rather inadequate to be directly applied on a database with incomplete data. In such cases, processing skyline queries on incomplete data incur exhaustive pairwise comparisons between data items, which may lead to loss of the transitivity property of the skyline technique. Losing the transitivity property may in turn give rise to the problem of cyclic dominance. In order to address these issues, we propose a new skyline algorithm called Sorting-based Cluster Skyline Algorithm (SCSA) that combines the sorting and partitioning techniques and simplifies the skyline computation on an incomplete dataset. These two techniques help boost the skyline process and avoid many unnecessary pairwise comparisons between data items to prune the dominated data items. The comprehensive experiments carried out on both synthetic and real-life datasets demonstrate the effectiveness and versatility of our approach as compared to the currently used approaches

Skyline queries computation on crowdsourced- enabled incomplete database

Data incompleteness becomes a frequent phenomenon in a large number of contemporary database applications such as web autonomous databases, big data, and crowd-sourced databases. Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. Most importantly, the skylines derived from incomplete databases are also incomplete in which some values are missing. Retrieving skylines with missing values is undesirable, particularly, for recommendation and decision-making systems. Furthermore, running skyline queries on a database with incomplete data raises a number of issues influence processing skyline queries such as losing the transitivity property of the skyline technique and cyclic dominance between the tuples. The issue of estimating the missing values of skylines has been discussed and examined in the database literature. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible values using the crowd. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However, task processing using crowd-sourced incurs additional monetary cost and increases the time latency. Also, it is not always possible to produce a satisfactory result that meets the user's preferences. This paper proposes an approach for estimating the missing values of the skylines by first exploiting the available data and utilizes the implicit relationships between the attributes in order to impute the missing values of the skylines. This process aims at reducing the number of values to be estimated using the crowd when local estimation is inappropriate. Intensive experiments on both synthetic and real datasets have been accomplished. The experimental results have proven that the proposed approach for estimating the missing values of the skylines over crowd-sourced enabled incomplete databases is scalable and outperforms the other existing approaches

An overview of query processing on crowdsourced databases

Crowd-sourcing is a powerful solution to correctly answer expensive and unanswered queries in the database. This includes queries on a database with uncertain and incomplete data. The crowd-sourcing attempts to exploit human abilities to process these difficult tasks and workers helped to provide accurate results utilizing the available data in the crowd. The crowd-sourcing database systems (CSDB) combined the ability of the crowd with the relational database by using some variant of the relational database with minor changes. This paper surveys and examines the leading studies conducted on the area of query processing for both traditional and preference queries in crowd-sourcing databases. The focus is given on highlights the strengths and the weakness of each approach. A detailed discussion for the current and future trends research relevant to query processing in the area of crow-sourced databases is also demonstrated

A Rule-based Skyline Computation over a dynamic database

Skyline query which relies on the notion of Pareto dominance filters the data items from a database by ensuring only those data items that are not worse than any others are selected as skylines. However, the dynamic nature of databases in which their states and/or structures change throughout their lifetime to incorporate the current and latest information of database applications, requires a new set of skylines to be derived. Blindly computing skylines on the new state/structure of a database is inefficient, as not all the data items are affected by the changes. Hence, this paper proposes a rule-based approach in tackling the above issue with the main aim at avoiding unnecessary skyline computations. Based on the type of operation that changes the state/structure of a database, i.e. insert/delete/update a data item(s) or add/remove a dimension(s), a set of rules are defined. Besides, the prominent dominance relationships when pairwise comparisons are performed are retained; which are then utilised in the process of computing a new set of skylines. Several analyses have been conducted to evaluate the performance and prove the efficiency of our proposed solution

A model for processing skyline queries in crowd-sourced databases

Nowadays, in most of the modern database applications, lots of critical queries and tasks cannot be completely addressed by machine. Crowd-sourcing database has become a new paradigm for harness human cognitive abilities to process these computer hard tasks. In particular, those problems that are difficult for machines but easier for humans can be solved better than ever, such as entity resolution, fuzzy matching for predicates and joins, and image recognition. Additionally, crowd-sourcing database allows performing database operators on incomplete data as human workers can be involved to provide estimated values during run-time. Skyline queries which received formidable attention by database community in the last decade, and exploited in a variety of applications such as multi-criteria decision making and decision support systems. Various works have been accomplished address the issues of skyline query in crowd-sourcing database. This includes a database with full and partial complete data. However, we argue that processing skyline queries with partial incomplete data in crowd-sourcing database has not received an appropriate attention. Therefore, an efficient approach processing skyline queries with partial incomplete data in crowd-sourcing database is needed. This paper attempts to present an efficient model tackling the issue of processing skyline queries in incomplete crowd-sourcing database. The main idea of the proposed model is exploiting the available data in the database to estimate the missing values. Besides, the model tries to explore the crowd-sourced database in order to provide more accurate results, when local database failed to provide precise values. In order to ensure high quality result could be obtained, certain factors should be considered for worker selection to carry out the task such as workers quality and the monetary cost. Other critical factors should be considered such as time latency to generate the results

Comparative Estimation of the Cytotoxic Activity of Different Parts of Cynara scolymus L.: Crude Extracts versus Green Synthesized Silver Nanoparticles with Apoptotic Investigation

Different parts of Cynara scolymus L. and their green synthesized eco-friendly silver nanoparticles (AgNPs) were screened for their cytotoxicity and apoptotic activity. Results showed that flower extract AgNPs exhibited more potent cytotoxicity compared to the normal form against PC-3 and A549 cell lines with IC50 values of 2.47 μg/mL and 1.35 μg/mL, respectively. The results were compared to doxorubicin (IC50 = 5.13 and 6.19 μg/mL, respectively). For apoptosis-induction, AgNPs prepared from the flower extract induced cell death by apoptosis by 41.34-fold change and induced necrotic cell death by 10.2-fold. Additionally, they induced total prostate apoptotic cell death by a 16.18-fold change, and it slightly induced necrotic cell death by 2.7-fold. Hence, green synthesized flower extract AgNPs exhibited cytotoxicity in A549 and PC-3 through apoptosis-induction in both cells. Consequently, synthesized AgNPs were further tested for apoptosis and increased gene and protein expression of pro-apoptotic markers while decreasing expression of anti-apoptotic genes. As a result, this formula may serve as a promising source for anti-cancer candidates. Finally, liquid chromatography combined with electrospray mass spectrometry (LC-ESI-MS) analysis was assessed to identify the common bioactive metabolites in crude extracts of stem, flower, and bract

Silver Nanoparticles Formulation of Flower Head’s Polyphenols of Cynara scolymus L.: A Promising Candidate against Prostate (PC-3) Cancer Cell Line through Apoptosis Activation

Cynara scolymus L. (Family: Compositae) or artichoke is a nutritious edible plant widely used for its hepatoprotective effect. Crude extracts of flower, bract, and stem were prepared and evaluated for their in vitro antioxidant activity and phenolic content. The flower crude extract exhibited the highest phenolic content (74.29 mg GAE/gm) as well as the best in vitro antioxidant activity using total antioxidant capacity (TAC), ferric reducing antioxidant power (FEAP), and 1,1-diphenyl-2-picrylhyazyl (DPPH) scavenging assays compared with ascorbic acid. Phenolic fractions of the crude extracts of different parts were separated and identified using high-performance liquid chromatography HPLC-DAD analysis. The silver nanoparticles of these phenolic fractions were established and tested for their cytotoxicity and apoptotic activity. Results showed that silver nanoparticles of a polyphenolic fraction of flower extract (Nano-TP/Flowers) exhibited potent cytotoxicity against prostate (PC-3) and lung (A549) cancer cell lines with IC50 values of 0.85 μg/mL and 0.94 μg/mL, respectively, compared with doxorubicin as a standard. For apoptosis-induction, Nano-TP/Flowers exhibited apoptosis in PC-3 with a higher ratio than in A549 cells. It induced total prostate apoptotic cell death by 227-fold change while it induced apoptosis in A549 cells by 15.6-fold change. Nano-TP/Flowers upregulated both pro-apoptotic markers and downregulated the antiapoptotic genes using RT-PCR. Hence, this extract may serve as a promising source for anti-prostate cancer candidates