So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top-$k$spatial textual cluster retrieval ($k$-STC) query that returns the top-$k$clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance.

SPARQL query services that balance processing between clients and servers become more and more essential to handle the increasing load for open and decentralized knowledge graphs on the Web. To this end, Linked Data Fragments (LDF) have introduced a foundational framework that has sparked research exploring a spectrum of potential Web querying interfaces in between server-side query processing via SPARQL endpoints and client-side query processing of data dumps. Current proposals in between typically suffer from imbalanced load on either the client or the server. In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. Our experiments show that WiseKG significantly outperforms state-of-the-art solutions in terms of average total query execution time per client, while at the same time decreasing network traffic and increasing server-side availability.

The Web of Data has grown explosively over the past few years, and as with any dataset, there are bound to be invalid statements in the data, as well as gaps. Natural Language Processing (NLP) is gaining interest to fill gaps in data by transforming (unstructured) text into structured data. However, there is currently a fundamental mismatch in approaches between Linked Data and NLP as the latter is often based on statistical methods, and the former on explicitly modelling knowledge. However, these fields can strengthen each other by joining forces. In this position paper, we argue that using linked data to validate the output of an NLP system, and using textual data to validate Linked Open Data (LOD) cloud statements is a promising research avenue. We illustrate our proposal with a proof of concept on a corpus of historical travel stories.

Spatial keyword querying has attracted considerable research efforts in the past few years. A prototypical query takes a location and keywords as arguments and returns the k objects that score the highest according to a ranking function. While different scoring functions have been used, how to compare different ranking functions for spatial keyword querying still remains an open question with little investigation. We propose a crowdsourcing-based approach to evaluate and compare ranking functions for spatial keyword search. Given two ranking functions f1 and f2, we use a matrix to model all possible binary questions regarding the different results produced by f1 and f2. We propose a multi-step process to reduce the number of binary questions, identifying the most important questions to ask. Further, we design a crowdsourcing model that obtains the answers to those important binary questions from crowd workers. We also devise a global evaluation process that is able to quantitatively compare f1 and f2 based on a multitude of answers received. According to the results of empirical studies using real data, the proposed approach is efficient and able to draw reliable conclusions in comparing ranking functions for spatial keyword search.

We demonstrate Crowd Rank Eval, a novel framework for the evaluation of ranking functions for top-k spatial keyword queries. The framework enables researchers to study hypotheses regarding ranking functions. Crowd Rank Eval uses crowd sourcing for synthesizing results to top-k queries and is able to visualize the results and to compare them to the results obtained from ranking functions, thus offering insight into the ranking functions.

In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user's location changes. We have developed sequential pattern mining-based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under small prediction sets.