Preference Queries Research Articles

Privacy-preserving top-[formula omitted] spatio-temporal keyword preference query

With the rapid advancement of location-based services and mobile devices, spatial keyword query attracts increasing attention. In this paper, we focus on a new query type known as top-k spatio-temporal keyword preference query. This kind of query considers both the spatial object itself and other spatial objects in the neighborhood to return k spatial objects with the highest score. These k spatial objects satisfy spatial and temporal constraints, while their scores are determined by the keyword similarity of the neighboring spatial objects. We propose a scheme to enable privacy-preserving top-k spatio-temporal keyword preference queries. To effectively represent temporal information, we employ time vectors to denote time periods, allowing us to assess whether the data satisfies temporal constraints based on the inner product of time vectors. Furthermore, we adopt a two-step strategy to execute the query. The first step is to find all Points of Interest (POIs) that meet the spatial and temporal constraints. The second step is to calculate the score of each POI and return the top-k POIs with the highest score. To enhance query efficiency, we build a tree index structure that can achieve sub-linear query complexity. Additionally, we utilize EASPE algorithm to encrypt both the index and the query, ensuring privacy-preserving capabilities. Security analysis proves that our scheme satisfies CQA2-security. At the same time, experimental evaluation validates the query performance of our scheme.

Abstract PO2-03-01: Personalized 3D tumor models drive shared decision-making in early breast cancer

Abstract Background: Decision-making for early (Stages 1-3) breast cancer (eBC) care is complex and highly personal, requiring discussions between patients and multiple physicians. Shared decision-making (SDM) is an accepted process that combines input from both patients and physicians, although its overall use is low due to a lack of time and incentives, complicated application techniques, and few communication tools. Reviews show that SDM results in “improved affective-cognitive outcomes” but evidence linking to patient outcomes is sparse. Consequently, patients often receive mixed messages and feel anxious when making treatment decisions. SimBioSys (SBS) developed the TumorSight(TM) platform for individualized treatment planning, coordinated care, and SDM. The first application on this platform, TumorSight Viz, rapidly builds 3D computational models from standard breast MRI data, and further integrates deep learning, genomic/genetic testing, metabolomics, and clinical and demographic data to tailor these models for each individual diagnosed with eBC. The platform can also transform acquired data (e.g., prone) to surgical position (e.g., supine) to create realistic images for precision surgical planning and tools for better SDM with eBC patients. We are assessing how surgeons and patient advocates (PA) perceive TumorSight, and how it may support better SDM and treatment implementation. Methods: SBS conducted an exploratory survey with 31 eBC surgeons to assess the utility of TumorSight Viz visualizations across a range of didactic and planning applications and to query preferences for future components like predicted response to chemotherapy and immunotherapy. Questions were developed with input from breast surgeon advisors. More exploration on TumorSight Viz utility is planned with semi-structured qualitative interviews to assess patient advocates’ perceptions of 3D visualizations, surgical planning, and ability to support decision making. PAs will represent geographic, experience, and race/ethnicity diversity. Questions to elicit patient needs, perception of SBS tools, and quality-of-life are being finalized with academic advisors. These will also be used to develop a future eBC patient survey. Results: The SBS surgeon survey revealed that 96% of breast surgeons find visualization important to surgical planning, and these depictions were considered to be critical by a third of breast surgeons surveyed. 96% also listed the conversion of prone to supine visualizations as important (45% critical, 35% valuable, 16% nice to have, 0% not important) for surgical planning value. Surgeons also assigned strong utility for: Graphical 3D rotation to determine tumor shape, size, location, positioning, and landmarks relative to other breast structures – 100% Ability to instantly provide tumor-to-breast volume determination with high accuracy – 96% Ability to instantly provide anticipated tumor extirpation volume to breast volume with high accuracy – 96% Automatic tumor-to-landmark structures measurements (nipple, chest, skin) – 96% Visualization of tumor in the context of other anatomical structures (e.g., skin, blood vessels, chest, heart) – 87% Initial PA discussions (n=3) listed the importance of individualized treatment based on evidence instead of hunches; reasons to avoid prophylactic mastectomies; and ways to lower patient anxiety. Conclusions: Survey-based feedback shows value to eBC surgeons and patients to coordinate care and personalize approaches for optimal outcomes. Computational tools can facilitate patient understanding and patient-tailored treatment. Additional surveys are planned with patients and eBC patient advocacy groups to assess the utility and potential expansion of patient-specific 3D tumor visualizations, both of which will be further discussed. Citation Format: Deborah Collyar, Matthew Biancalana, Anuja Antony, Nicole Liadis, Jyoti Palaniappan, Rebecca Seago-Coyle. Personalized 3D tumor models drive shared decision-making in early breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-03-01.

Early Perspectives: Exploring the Potential Impacts of Autonomous Vehicles Through the Lens of Urban Mobility and Urban Form

The emergence of autonomous vehicles (AVs) is expected to significantly reshape urban mobility and travel behavior patterns. AVs have the potential to offer higher levels of convenience, safety, and accessibility while enabling users to spend their trip time on more efficient and productive tasks such as working or even relaxing. This transformation in the short- to mid-term could result in changes in the public's sensitivity and perception toward enduring longer travel times and consequently, in mid- to long-term, it could influence the willingness to reconsider their residential locations. Therefore, the objective of this study is to enhance the understanding of the potential effects of AVs on travel behavior and land use through the examination of stated preference queries. To achieve this objective, various multinomial logit models toward AVs adoption and residential relocation were estimated by employing a dataset from Istanbul, Türkiye, as an example of megacity in a developing country. While the study findings revealed a set of potential adoption barriers for AVs, they also indicated a notable propensity for adopting these vehicles. Furthermore, concerning individuals' willingness to reconsider their residential locations due to the emergence of AVs, a considerable positive attitude was evident, albeit guarded. This attitude suggests that individuals' decisions are still evolving and can be guided toward the desired future through well-timed and well-suited policies. The outcomes of the study can serve as valuable input for policymakers as well as transportation and urban planners, offering insights into the potential impacts of AVs on urban mobility and form.

A Privacy Protection Method Based on Trajectory Location Point Association

Location-based services (LBS) technology provides personalized services for users. However, private location information faces the risk of being leaked while users are enjoying the LBS services. In order to prevent this from occurring, this study proposes an anonymous privacy protection method based on trajectory location association, abbreviated as AS-AP, which considers the association between the user’s current location and the background information. The association between locations is mined from time and query preferences, and then fake locations and queries are generated as part of the anonymous interference requests. This conforms to the k-anonymity feature, which prevents attackers from inferring the user's real intent from the network information, thereby enhancing the credibility of network technology, and protecting data privacy. The experimental results indicate that AS-AP can provide more personalized privacy protection services while ensuring that users' sensitive data is protected.

Query Preference Analysis on Cascade Inference Human–Object Interaction Detection Transformer

Detection transformers (DETR) have provided a novel solution to human–object interaction detection in a set-prediction manner, thanks to expressive learnable queries. However, few studies covered how queries implicitly affect model behaviors, which might contain clues for model improvement. Therefore, we propose two dataset-based analysis tools: score state space and query preference map. They provide data on the distribution of model predictions, which can reveal overall-level and query-level model properties. Starting with our baseline model, we find the model naturally regresses object boxes to overlap human boxes in the edge case of no-object verbs (stand, etc.), even without a related loss. We encourage this by patching the supervision with virtual objects, resulting in more stable query preference. Moreover, we show that two-stage decoders designed for cascade inference do not decouple tasks as intended. We infer this is caused by the empty instances used as negative samples, which suggests a redesign in the matching scheme. Further, we reveal how adding an oracle-query-based teacher model affects query roles with a tiny gain, indicating room for refinement. Our findings demonstrate how a simple focus on query behaviors can provide insights for improving models.

Handling qualitative conditional preference queries in SPARQL: possibilistic logic approach

PurposeThe purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.Design/methodology/approachThe paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.FindingsThe paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.Originality/valueThe paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory.

The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like themselves, which may not hold true for all drivers. To address this issue, this study proposes an online personalized preference learning method (OPPLM) that utilizes a pairwise comparison group preference query and the Bayesian approach. The proposed OPPLM adopts a two-layer hierarchical structure model based on utility theory to represent driver preferences on the trajectory. To improve the accuracy of learning, the uncertainty of driver query answers is modeled. In addition, informative query and greedy query selection methods are used to improve learning speed. To determine when the driver's preferred trajectory has been found, a convergence criterion is proposed. To evaluate the effectiveness of the OPPLM, a user study is conducted to learn the driver's preferred trajectory in the curve of the lane centering control (LCC) system. The results show that the OPPLM can converge quickly, requiring only about 11 queries on average. Moreover, it accurately learned the driver's favorite trajectory, and the estimated utility of the driver preference model is highly consistent with the subject evaluation score.

A preference elicitation framework for automated planning

Many automated planning systems allow users to specify their preferences in order to generate plans that are of high quality according to specified preferences. However, specifying preferences upfront can be time-consuming and difficult for users as their preferences can be complex, not be known in advance, or largely incomplete, especially in complex and large applications. Although many preference representation models and elicitation methods have been proposed in the literature, their adaptation to planning is non-trivial and work in this area has been somewhat limited. In this work, we present a preference elicitation framework for automated planning. This framework allows for interaction with a user through a limited number of low burden comparison type queries and for subsequent learning of a preference relation predictor based on the user’s responses. To help the user explore possible planning solutions and to assist them to easily discover and articulate their preferences, the framework prompts the user with planning solutions for small problems and attempts to learn the user’s preference through a variety of small planning problems from a particular domain. The learned preferences can then be generalised to infer preferences for planning solutions to larger planning problems. We evaluate the framework using a variety of different benchmark planning domains. The results suggest that our framework can efficiently learn user preferences when provided with a limited number of preference queries in small problems, and generalise the user preferences with high accuracy to larger planning problems.

Handling qualitative preferences in SPARQL over virtual ontology-based data access

With the increase of data volume in heterogeneous datasets that are being published following Open Data initiatives, new operators are necessary to help users to find the subset of data that best satisfies their preference criteria. Quantitative approaches such as top-k queries may not be the most appropriate approaches as they require the user to assign weights that may not be known beforehand to a scoring function. Unlike the quantitative approach, under the qualitative approach, which includes the well-known skyline, preference criteria are more intuitive in certain cases and can be expressed more naturally. In this paper, we address the problem of evaluating SPARQL qualitative preference queries over an Ontology-Based Data Access (OBDA) approach, which provides uniform access over multiple and heterogeneous data sources. Our main contribution is Morph-Skyline++, a framework for processing SPARQL qualitative preferences by directly querying relational databases. Our framework implements a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. We evaluate our approach over different scenarios, reporting the effects of data distribution, data size, and query complexity on the performance of our proposed technique in comparison with state-of-the-art techniques. Obtained results suggest that the execution time can be reduced by up to two orders of magnitude in comparison to current techniques scaling up to larger datasets while identifying precisely the result set.

A General Interactive Approach for Solving Multi-Objective Combinatorial Optimization Problems with Imprecise Preferences

In this paper, we develop a general interactive method to solve multi-objective combinatorial optimization problems with imprecise preferences. Assuming that preferences can be represented by a parameterized scalarizing function, we iteratively ask preferences queries to the decision maker in order to reduce the uncertainty over the preference parameters until being able to determine her preferred solution. To produce informative preference queries at each step, we generate promising solutions using the extreme points of the polyhedron representing the admissible preference parameters and then we ask the decision maker to compare two of these solutions (we propose different selection strategies). These extreme points are also used to provide a stopping criterion guaranteeing that the returned solution is optimal (or near-optimal) according to the decision maker's preferences. For the multi-objective spanning tree problem with a linear aggregation function, we provide numerical results to demonstrate the practical efficiency of our approach and we compare our results to a recent approach based on minimax regret, where preferences are asked during the construction of a solution. We show that better results are achieved by our method both in terms of running time and number of questions.

Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences

Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstrations (e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings) are actively elicited. Prior research has independently applied reward learning to these different data sources. However, there exist many domains where multiple sources are complementary and expressive. Motivated by this general problem, we present a framework to integrate multiple sources of information, which are either passively or actively collected from human users. In particular, we present an algorithm that first utilizes user demonstrations to initialize a belief about the reward function, and then actively probes the user with preference queries to zero-in on their true reward. This algorithm not only enables us combine multiple data sources, but it also informs the robot when it should leverage each type of information. Further, our approach accounts for the human’s ability to provide data: yielding user-friendly preference queries which are also theoretically optimal. Our extensive simulated experiments and user studies on a Fetch mobile manipulator demonstrate the superiority and the usability of our integrated framework.

Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization

We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. More precisely, for linear objective (utility) functions, we propose an interactive greedy algorithm interleaving preference queries with the incremental construction of an independent set to obtain an optimal or near-optimal base of a matroid. We also propose an interactive local search algorithm based on sequences of possibly improving exchanges for the same problem. For both algorithms, we provide performance guarantees on the quality of the returned solutions and the number of queries. Our algorithms are tested on the uniform, graphical and scheduling matroids to solve three different problems (committee election, spanning tree, and scheduling problems) and evaluated in terms of computation times, number of queries, and empirical error.

Preference-Aware Edge Server Placement in the Internet of Things

While it is well understood that edge computing can significantly facilitate IoT-related applications by deploying edge servers close to IoT devices, it also faces many challenges with numerous IoT devices connected and interacted. One of the most important issues is how to efficiently deploy edge servers under a certain budget with the explosive growth of data scale and user base. Existing studies for edge server placement fail to consider user’s query preferences since individual users may be interested in events in particular regions and are keen to receive up-to-date data streams that originate in regions of interest. In this article, we present a preference-aware edge server placement approach that offers better workload distribution in terms of both minimizing query latency and balancing the load of edge servers. To achieve this, we formulate edge server placement with multiobjective optimization as a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -center problem and design two progressive approaches. We first propose quadratic integer programming (QIP) for small-scale data sets. Since the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -center problem is an NP-hard problem, we thus propose a heuristic algorithm named TAKG (TAbu search with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means and Genetic algorithm) for large-scale data sets. To evaluate the utility of the proposed models, we have conducted a comprehensive evaluation on a large data set that is collected by more than 1900 IoT devices during 30 days. Experimental results indicate our approaches outperform all baselines significantly in terms of both query latency and load balancing.

Research on K Nearest Neighbor Skyline Query in Time Dependent Road Network

In recent years, with the increase for query preference, skyline query in road networks has gradually become a research hot topic. In order to help users get the desired query result, we propose a new algorithm. Firstly, we select landmark heuristically and establish corresponding shortest path tree (SPT). Then we construct a skyline matrix index called time dependent matrix index (TDMI), which help us associate the updated result set with dynamic k nearest neighbor (KNN) and simplify the non-space dominance relationship better. Finally, with SPT, TDMI, KNN and dynamic pruning conditions, an algorithm, the TDMI-KS algorithm, is proposed to rapidly answer the query result. Extensive experiments using Oldenburg road network datasets demonstrate the effectiveness and the efficiency of our proposed TDMI-KS algorithms.

Creating a cancer genomics curriculum for pediatric hematology-oncology fellows: A national needs assessment.

BackgroundWith the advent of next generation sequencing, tumor and germline genomic testing are increasingly being used in the management of pediatric cancer patients. Despite this increase in testing, many pediatric hematology–oncology (PHO) providers are not confident interpreting or utilizing tumor or germline genomic results to care for their patients.MethodsWe developed and delivered a needs assessment survey to PHO program directors, attendings, and fellows in the United States to understand this deficiency, gather data on existing cancer genomics educational initiatives, and query preferences for creating a future curriculum.ResultsThe survey includes 31 (41%) of 74 invited PHO program directors, 110 (11%) of 1032 invited attendings, and 79 fellows. The majority of attending physicians and fellows responding to the survey agree that understanding tumor (95% attending physicians; 95% fellows) and germline (86% attending physicians; 94% fellows) genomic information is essential for their practice. However, only 9 of 31 (29%) responding programs report that they have an existing cancer genomics curriculum. Most program directors indicated that the ideal genomics curriculum would occur during the first year of fellowship and incorporate direct patient care, online modules, and problem‐based learning. Attending physicians and fellows identified that addressing indications for ordering tumor and germline genomic testing, counseling about the risks and benefits of such testing, and interpreting and individualizing clinical management based on tumor and germline results should be included in a future curriculum.ConclusionThe results of this study reveal a great need to develop a curriculum that can be offered across PHO fellowship programs to expand knowledge in the area of cancer genomics.

Efficient Skyline Computation on Uncertain Dimensions

The database community has observed in the past two decades, the growth of research interest in preference queries, each of which has its unique techniques, benefits, and drawbacks. One of them is skyline queries. Skyline queries aim to report to users interesting objects based on their preferences. Yet, they are not without their limitations. Hence, this paper focuses on efficiently extending skyline query processing to support the uncertainty in dimensions, which in this paper is defined as uncertain dimension. To process skyline queries on data with uncertain dimensions, we propose SkyQUD algorithm, where it provides a mechanism that will partition the dataset according to the characteristics of each object before skyline dominance tests are performed. In the pruning process, we utilise a probability threshold value τ to accommodate the large skyline size reported by SkyQUD due to the computed probabilities. The algorithm has been validated through extensive experiments. Its results exhibit that skyline queries can be performed effectively on uncertain dimensions, and the proposed algorithm is efficient in query answering and capable of handling large datasets.

Knowledge Graph-Based Spatial-Aware User Community Preference Query Algorithm for LBSNs

Abstract User community preference in Location-Based Social Networks (LBSNs) can meet the diversified location demands of group LBSN users. Although individual's location-based service recommendation or personal spatial preference query problem has been well addressed by many studies, user group or user community preference query is still under way and most only consider the spatial distance factor, which causes accuracy cannot satisfy user demands. To solve the user community spatial preference problem and improve its performance, a knowledge graph-based spatial-aware user community preference query algorithm, Type R-tree (tR-tree) Query Algorithm (TRQA) is proposed to effectively discover user's community preference from LBSNs considering both location semantic information and preference weight of users' Points of Interest (POIs). To achieve this goal, this paper first leverages the tR-tree spatial index to improve query efficiency. Then a community satisfaction degree model based on knowledge graphs is introduced to comprehensively evaluate whether the POI can best meet the preference requirements of a user community. The experimental results show that TRQA has outperformed Perceptual Quality Adaptation Algorithm (PQA) in terms of pruning efficiency and query time. The query time of our proposed algorithm is 80% shorter than PQA as the number of users in the user community changes.

Personalizing the Top-k Spatial Keyword Preference Query with textual classifiers

The volume of data available online has increased considerably in the last years. Among this extensive amount, there is a specific kind called spatial data – a representation of a physical object using its spatial coordinates (e.g. latitude and longitude). Spatial queries are widely employed to manipulate spatial data more efficiently. However, the user has a crucial role in the spatial information retrieval process when querying the needed information. During the last few decades, researchers have proposed several techniques to aid users to express the information needed, such as boolean models, pattern matching operators, and query expansion. Despite the existence of significant alternatives to express the information need, there is still a lack of solutions applied to keyword preference queries. In this context, this work proposes a personalization approach to improve the Top-k Spatial Keyword Preference Query (SKPQ) results. By exploiting reviews on points of interest, the system identifies points which satisfy the user to order the result set with respect to his/her preference. The SKPQ result is compared to the one generated by our proposal using the OpinRank dataset. The assessment indicates a relative NDCG (Normalized Discounted Cumulative Gain) improvement of the proposed approach over the SKPQ of 92% when using random query keywords, and 33% when using frequent keywords. This personalization approach can be applied to any spatial keyword query since it re-orders the results instead of modifying the query processing.

Efficient learning of personalized visual preferences in daylit offices: An online elicitation framework

Human preference-based control in buildings may achieve maximum occupant satisfaction as well as energy savings. Adaptive and online learning methods are needed for learning human preferences, using the minimum amount of data possible with quantified uncertainty. This paper presents an online visual preference elicitation learning framework, developed for efficiently learning occupants' visual preference profiles and hidden satisfaction utility functions in daylit offices. A combination of Thompson sampling and pure exploration (uncertainty learning) methods was used in the sequential elicitation framework, to determine the set of successive visual preference queries (for visual conditions) with most information gain. In this way, a balance between exploration and exploitation is realized and the area around the satisfaction utility maximum is learned with minimum number of steps.Experiments with human subjects were conducted in private sidelit offices to demonstrate the feasibility and performance of the learning framework. A single-variable model (vertical illuminance) was used to demonstrate the method and visualize results. The entropy of the distribution of the most preferred visual condition is computed for each learned preference profile to quantify the certainty and evaluate the learning efficiency. Our method learns most individual visual preferences to an acceptable certainty level within one day, and indicates the need for personalization. Finally, we discuss the integration of visual preferences into control applications. A switching algorithm is proposed, shifting iteratively between the learning and control modes depending on the certainty of the learned preference model. This work contributes to developing comprehensive online learning methods towards preference-based tuned indoor environments.

An Interactive Regret-Based Genetic Algorithm for Solving Multi-Objective Combinatorial Optimization Problems

We propose a new approach consisting in combining genetic algorithms and regret-based incremental preference elicitation for solving multi-objective combinatorial optimization problems with unknown preferences. For the purpose of elicitation, we assume that the decision maker's preferences can be represented by a parameterized scalarizing function but the parameters are initially not known. Instead, the parameter imprecision is progressively reduced by asking preference queries to the decision maker during the search to help identify the best solutions within a population. Our algorithm, called RIGA, can be applied to any multi-objective combinatorial optimization problem provided that the scalarizing function is linear in its parameters and that a (near-)optimal solution can be efficiently determined when preferences are known. Moreover, RIGA runs in polynomial time while asking no more than a polynomial number of queries. For the multi-objective traveling salesman problem, we provide numerical results showing its practical efficiency in terms of number of queries, computation time and gap to optimality.