ANSWERING WHY-NOT QUESTIONS ON REVERSE SKYLINE QUERIES OVER INCOMPLETE DATA

        Recently, the development of the query-based preferences has received considerable attention from researchers and data users. One of the most popular preference-based queries is the skyline query, which will give a subset of superior records that are not dominated by any other records. As the developed version of skyline queries, a reverse skyline query rise. This query aims to get information about the query points that make a data or record as the part of result of their skyline query.     Furthermore, data-oriented IT development requires scientists to be able to process data in all conditions. In the real world, there exist incomplete multidimensional data, both because of damage, loss, and privacy. In order to increase the usability over a data set, this study will discuss one of the problems in processing reverse skyline queries over incomplete data, namely the "why-not" problem. The considered solution to this "why-not" problem is advice and steps so that a query point that does not initially consider an incomplete data, as a result, can later make the record or incomplete data as part of the results. In this study, there will be further discussion about the dominance relationship between incomplete data along with the solution of the problem. Moreover, some performance evaluations are conducted to measure the level of efficiency and effectiveness

Menjawab Why-Not Question Pada K-Most Promising Product (K-MPP) Dengan Pendekatan Data Refinement

K-Most Promising Product (K-MPP) adalah strategi product selection yang digunakan pada proses pencarian K-produk yang paling banyak diminati oleh customer. Dasar komputasi yang digunakan untuk melakukan perhitungan K-MPP adalah dua tipe skyline query, yaitu: dynamic skyline dan reverse skyline. Penentuan K-MPP dilakukan pada layer aplikasi, yang merupakan layer paling atas pada model OSI. Salah satu fungsi layer aplikasi adalah untuk menyediakan layanan terbaik sesuai dengan keinginan user. Dalam implementasi K-MPP, akan muncul suatu keadaan dimana produsen mungkin kurang puas dengan query result yang dihasilkan pada proses pencarian di sistem database (why-not question), sehingga mereka juga ingin mengetahui mengapa sistem database memberikan hasil pencarian query yang tidak sesuai dengan harapannya. Sebagai contoh, produsen ingin mengetahui mengapa suatu data point tertentu yang tidak diharapkan (unexpected data) muncul di query result, dan mengapa produk yang diharapkan (expected data) tidak muncul sebagai query result. Permasalahan yang muncul selanjutnya adalah, sistem database tradisional tidak dapat memberikan fasilitas analisis data dan solusi untuk menjawab why-not question yang diajukan oleh user. Untuk meningkatkan usability pada sistem database, penelitian ini dilakukan dengan tujuan menjawab why-not K-MPP dan memberikan solusi berupa data refinement dengan mempertimbangkan user feedback sehingga user dapat mengetahui mengapa himpunan hasil yang muncul tidak sesuai dengan harapan, dan dapat membantu user untuk memahami serta mengubah query agar menghasilkan query result sesuai keinginan user namun dengan cost perubahan seminimal mungkin. Berdasarkan proses evaluasi yang telah dilakukan terhadap tiga jenis tipe data yang berbeda, yaitu: independen, anti-correlated, dan forest cover type rata-rata waktu yang dibutuhkan untuk mencari variasi data refinement cenderung konstan dan akan mengalami peningkatan pada kardinalitas data dan selisih K yang tinggi. Rentang waktu yang dibutuhkan berada pada nilai 1.13 s hingga 3.48 s, dimana besarnya nilai rata-rata waktu dipengaruhi oleh jumlah data, dimensi data, dan selisih K antara K-MPP dan why-not point. ====================================================================================================== K-Most Promising (K-MPP) product is an optional product selection strategy that used in the process of determining the most demanded products by consumers. The basic computations used to perform K-MPP calculations are two types of skyline queries: dynamic skyline and reverse skyline. K-MPP selection performed on the application layer, which is the last layer of the OSI model. One of the application layer functions is to provide services as the user's preferences. In the K-MPP implementation, there will be a situation in which the Manufacturer may be less satisfied with the query results generated by the database search process (why-not question), so they also want to know why the database gives query results that do not match their expectations. For example, manufacturers want to know why a particular data point (unexpected data) appears in the query result set, and why the expected product (expected data) does not appear as a query result. The next problem is that traditional database systems will not able to provide data analysis and solution to answer why-not questions preferred by users. To improve the usability of the database system, this study was conducted with the aim of answering why-not K-MPP and provide data refinement solutions by considering user feedback, so users can also find out why the result set does not meet their expectations, and help users to understand the result by performing analysis information and data refinement suggestion. Based on the evaluation process that has been done on three different types of data, namely: independent, anti-correlated, and forest cover type, the average time needed to find variations in data refinement tends to be constant and will increase in a large number of data cardinality and ∆K. The average evaluation time needed is vary from 1.13 s to 3.48 s, and it is influenced by the amount of data, data dimensions, and K difference between K-MPP and why-not points

Finding causality and responsibility for probabilistic reverse skyline query non-answers

NSF

EFQ: Why-Not Answer Polynomials in Action

International audienceOne important issue in modern database applications is supporting the user with efficient tools to debug and fix queries because such tasks are both time and skill demanding. One particular problem is known as Why-Not question and focusses on the reasons for missing tuples from query results. The EFQ platform demonstrated here has been designed in this context to efficiently leverage Why-Not Answers polynomials, a novel approach that provides the user with complete explanations to Why-Not questions and allows for automatic, relevant query refinements

Location- and keyword-based querying of geo-textual data: a survey

With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityThis research was supported in part by MOE Tier-2 Grant MOE2019-T2-2-181, MOE Tier-1 Grant RG114/19, an NTU ACE Grant, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant, and by the Innovation Fund Denmark centre, DIREC

How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Consider a variant of Tetris played on a board of width $w$ and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of $O(n)$ rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on a board of width $w=\Theta(n)$, if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time $O(n^{1/2-\epsilon})$ simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in $O(n^{1/2}\log^{3/2}n)$ time on boards of width $n^{O(1)}$, matching the lower bound up to a $n^{o(1)}$ factor.Comment: Correction of typos and other minor correction