Finding the underlying schema in knowledge graphs is an imperative operation for various tasks, such as query formulation or exploration. This task becomes even harder, when data are incomplete, noisy or are collected via multiple sources with different schemata that are combined. Several algorithms for extracting an implicit schema from a given knowledge graph have been proposed in the literature. However, the lack of a common framework and evaluation metrics makes it difficult to combine them and compare the results. To fill this gap, we present a modular three-stage framework and we have developed a Python library and web application that performs schema extraction and allows users to visually assess and compare the results. The developed tool, called KGSEC, facilitates experimentation and increases interactivity. Given that the quality of a schema is largely subjective, depending on the user's needs and preferences, KGSEC can make it easier and faster for users to generate a schema that is better tailored to their task.

Entity Resolution has been an active research topic for the last three decades, with numerous algorithms proposed in the literature. However, putting them into practice is often a complex task that requires implementing, combining and configuring complementary individual algorithms into comprehensive end-to-end workflows. To facilitate this process, we are developing pyJedAI, a novel system that provides a unifying framework for any type of main works in the field (i.e., both unsupervised and learning-based ones). Our vision is to facilitate both novice and expert users to use and combine these algorithms through a series of principled approaches for automatically configuring and benchmarking end-to-end pipelines.

Heterogeneous information networks (HINs) represent different types of entities and relationships between them. Exploring and mining HINs relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. In this paper, we present Atrapos, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. Atrapos selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. Our experimental study on real data shows that Atrapos accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios.

Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained embeddings have been tested, with the most popular ones being fastText and variants of the BERT model. However, there is no detailed analysis of their pros and cons. To cover this gap, we perform a thorough experimental analysis of 12 popular language models over 17 established benchmark datasets. First, we assess their vectorization overhead for converting all input entities into dense embeddings vectors. Second, we investigate their blocking performance, performing a detailed scalability analysis, and comparing them with the state-of-the-art deep learning-based blocking method. Third, we conclude with their relative performance for both supervised and unsupervised matching. Our experimental results provide novel insights into the strengths and weaknesses of the main language models, facilitating researchers and practitioners to select the most suitable ones in practice.

Analyzing time series data is crucial for many applications. In particular, subsequence search refers to finding subsequences within an input time series T that are similar to a query sequence Q. Existing subsequence search approaches typically employ Euclidean distance or Dynamic Time Warping as similarity measures and address range queries. In this paper, we focus on Chebyshev distance, which is the largest difference between each individual pair of points across the entire length of two compared subsequences. We call such similar pairstwins. We first show how existing time series indices can be extended to perform twin subsequence search. Then, we introduce TS-Index, a novel index tailored to the computation of twin subsequence search queries. Moreover, given that specifying a distance threshold is often not straightforward, we show how TS-Index can also be used to evaluate kNN queries. Our extensive experimental evaluation compares these approaches using real time series datasets. The results demonstrate that TS-Index can retrieve twin subsequences faster than all other methods under various conditions.

Set similarity join is an important problem with many applications in data discovery, cleaning and integration. To increase robustness, fuzzy set similarity join calculates the similarity of two sets based on maximum weighted bipartite matching instead of set overlap. This allows pairs of elements, represented as sets or strings, to also match approximately rather than exactly, e.g., based on Jaccard similarity or edit distance. However, this significantly increases the verification cost, making even more important the need for efficient and effective filtering techniques to reduce the number of candidate pairs. The current state-of-the-art algorithm relies on similarity computations between pairs of elements to filter candidates. In this paper, we propose token-based instead of element-based filtering, showing that it is significantly more lightweight, while offering similar or even better pruning effectiveness. Moreover, we address the top- k variant of the problem, alleviating the need for a user-specified similarity threshold. We also propose early termination to reduce the cost of verification. Our experimental results on six real-world datasets show that our approach always outperforms the state of the art, being an order of magnitude faster on average.

Given a collection of geospatial points of different types, mixture-based best region search aims at discovering spatial regions exhibiting either very high or very low mixture with respect to the types of enclosed points. Existing works detect fixed-shape regions, such as circles or rectangles, thus often missing interesting regions occurring in real-world data that may have arbitrary shapes. In this paper, we formulate the problem of mixture-based best region search for arbitrarily shaped regions, introducing certain desired properties to ensure their cohesiveness and completeness. Since computing exact solutions to this problem has exponential cost with respect to the number of points, we propose anytime algorithms that efficiently search the space of candidate solutions to produce high-scoring regions under any given time budget. Our experiments on several real-world datasets show that our algorithms can produce high-quality results even within tight time constraints.

In this demonstration, we present DatAgent , an intelligent data assistant system that allows users to ask queries in natural language, and can respond in natural language as well. Moreover, the system actively guides the user using different types of recommendations and hints, and learns from user actions. We will demonstrate different exploration scenarios that show how the system and the user engage in a human-like interaction inspired by the interaction paradigm of chatbots and virtual assistants.

JedAI is an open-source toolkit that allows for building and benchmarking thousands of schema-agnostic Entity Resolution (ER) pipelines through a non-learning, blocking-based end-to-end workflow. In this paper, we present its latest release, JedAI3 , which conveys two new end-to-end workflows: one for budgetagnostic ER that is based on similarity joins, and one for budgetaware (i.e., progressive) ER. This version also adds support for pre-trained word or character embeddings and connects JedAI to the Python data analysis ecosystem. Overall, these enhancements provide JedAI with features offered by no other ER tool, especially in the schema- and domain-agnostic context.

Given a set of points, the Best Region Search problem finds the optimal location of a rectangle of a specified size such that the value of a user-defined scoring function over its enclosed points is maximized. A recently proposed top-k algorithm for this problem returns results progressively, while also incorporating additional constraints, such as taking into consideration the overlap between the set of selected top-k rectangles. However, the algorithm is designed for a centralized setting and does not scale to very large datasets. In this paper, we overcome this limitation by enabling parallel and distributed computation of the results. We first propose a strategy that employs multiple rounds to progressively collect partial top-k results from each node in the cluster, while a coordinator handles the aggregation of the global top-k list, dealing with overlapping results. We then devise a single-round strategy, where the algorithm executed by each node is enhanced with additional conditions that anticipate potential overlapping solutions from neighboring nodes. Additional optimizations are proposed to further increase performance. Our experiments on real-world datasets indicate that our proposed algorithms are efficient and scale to millions of points.

We present spaTScope, a web application for visual exploration of geolocated time series. Analyzing such data is becoming increasingly important in many domains, such as energy demand management, geomarketing and geosocial networks. spaTScope allows users to visually explore large collections of geolocated time series and obtain insights about trends and patterns in their area of interest. The provided functionalities leverage a hybrid index that allows to navigate and group the available time series based not only on their similarity but also on spatial proximity. The results are visualized using linked plots combining maps and timelines.

We present SPHINX, a system for metapath-based entity exploration in Heterogeneous Information Networks (HINs). SPHINX allows users to define different views over a HIN based on both automatically selected and user-defined meta-paths. Then, entity ranking and similarity search can be performed over these views to find and explore entities of interest, taking also into account any spatial or temporal properties of entities. A Web-based user interface is provided to facilitate users in performing the various functionalities supported by the system, including metapath-based view definition, index construction, search parameters specification, and visual comparison of the results.

Geolocated time series, i.e., time series associated with certain locations, abound in many modern applications. In this paper, we consider hybrid queries for retrieving geolocated time series based on filters that combine spatial distance and time series similarity. For the latter, unlike existing work, we allow filtering based on local similarity, which is computed based on subsequences rather than the entire length of each series, thus allowing the discovery of more fine-grained trends and patterns. To efficiently support such queries, we first leverage the state-of-the-art BTSR-tree index, which utilizes bounds over both the locations and the shapes of time series to prune the search space. Moreover, we propose optimizations that check at specific timestamps to identify candidate time series that may exceed the required local similarity threshold. To further increase pruning power, we introduce the SBTSR-tree index, an extension to BTSR-tree, which additionally segments the time series temporally, allowing the construction of tighter bounds. Our experimental results on several real-world datasets demonstrate that SBTSR-tree can provide answers much faster for all examined query types.

Time series exploration and mining has many applications across several industrial and scientific domains. In this paper, we consider the problem of detecting locally similar pairs and groups, called bundles, over co-evolving time series. These are pairs or groups of subsequences whose values do not differ by more than ε for at least δ consecutive timestamps, thus indicating common local patterns and trends. We first present a baseline algorithm that performs a sweep line scan across all timestamps to identify matches. Then, we propose a filter-verification technique that only examines candidate matches at judiciously chosen checkpoints across time. Specifically, we introduce two block scanning algorithms for discovering local pairs and bundles respectively, which leverage the potential of checkpoints to aggressively prune the search space. We experimentally evaluate our methods against real-world and synthetic datasets, demonstrating a speed-up in execution time by an order of magnitude over the baseline.

In this demonstration, we present LGM-PC, a QGIS plugin for automatic recommendation of categories for new POIs. LGM-PC allows users to train classification models on individual areas of POIs and, then, use these models in order to classify new POIs into categories. The tool produces ranked category recommendations, based solely on the name of the POI, its coordinates and properties of its surrounding POIs, which are already annotated with categories. The user is then required to validate the produced recommendations, by selecting the most fitting category. Being implemented as a QGIS plugin, LGM-PC allows the visualization of POIs on map layers, for further assisting the user in the final category selection task.

Point of Interest (POI) data is widely used in many modern applications and services related to navigation, tourism, social networking, logistics, and many more. In this paper, we propose a comprehensive and vendor-agnostic data model to represent multi-faceted and enriched POI profiles. Harnessing the versatility of Linked Data technologies, this semantically rich ontology accommodates and extends existing POI formats for assembling and managing POI data from heterogeneous sources. Furthermore, we have developed the open-source software TripleGeo, which can effectively transform POI data from diverse sources and formats (geographical files, databases, and semi-structured data) to their RDF representations and vice versa. Thus, it is possible to import POI data from various existing systems and products, transfer and address the data integration challenges in the Linked Data domain, and export back the results. Our empirical study confirms the validity and efficiency of this framework for a variety of real-world POI assets and formats, underscoring its robustness to cope with scalable data volumes.

A geolocated time series is a sequence of values associated with a geolocation, such as measurements provided by a sensor installed at a certain location. In this paper, we address the problem of hybrid similarity joins over such geolocated time series. This operation returns all pairs of geolocated time series that exhibit similar behavior in the time series domain while also being closely located in space. First, we propose algorithms for performing such join operations using different types of indices, including spatial-only, time series-only, and hybrid indices. Such centralized indexing schemes can cope well with moderate data volumes but they face scalability issues when the dataset size increases significantly. To overcome this problem, we present a MapReduce-based processing scheme with space-driven partitioning. Our parallel and distributed algorithm leverages our hybrid index for geolocated time series to efficiently execute similarity joins locally within each partition and minimize the amount of data that needs to be shuffled between processing nodes. An extensive experimental evaluation confirms that our approach can efficiently compute all matching pairs even for datasets containing millions of geolocated time series.

Given a set of geospatial objects, the Best Region Search problem finds the optimal placement of a fixed-size rectangle so that the value of a user-defined utility function over the enclosed objects is maximized. The existing algorithm for this problem computes only the top result. However, this is often quite restrictive in practice and falls short in providing sufficient insight about the dataset. In this paper, we introduce the k-BRS problem, and we present a method for efficiently and progressively computing the next best result for any number k of results requested by the user. We show that our approach can accommodate additional constraints. In particular, we consider the requirement of computing the next best rectangle that has no or little overlap with the already retrieved ones, which reduces the repetition and redundancy in the results presented to the user. Our experimental evaluation demonstrates that our algorithms are efficient and scalable to large real-world datasets.

Millions of users rely on navigation applications to compute an optimal route for their trips. The basic functionality of these applications is to find the minimum cost route between a source and target node in the transportation network. In this paper, we address a variant of this problem, where the computed route is required to contain a set of Points of Interest of specific types. Our approach is based on the concept of keyword skyline. We formally define this concept, and we show how to compute the keyword skyline for the vertices of a given network and how to use it for computing the shortest keyword covering paths. We present different variants of this method, including an approximation algorithm, providing different trade-offs between preprocessing cost and execution time. Finally, we present an experimental evaluation of our approach using real-world datasets of different sizes, including also a comparison to the current state-of-the-art algorithm for this problem.

Thousands of posts are generated constantly by millions of users in social media, with an increasing portion of this content being geotagged. Keeping track of the whole stream of this spatio-textual content can easily become overwhelming for the user. In this paper, we address the problem of selecting a small, representative and diversified subset of posts, which is continuously updated over a sliding window. Each such subset can be considered as a concise summary of the stream's contents within the respective time interval, being dynamically updated every time the window slides to reflect newly arrived and expired posts. We define the criteria for selecting the contents of each summary, and we present several alternative strategies for summary construction and maintenance that provide different trade-offs between information quality and performance. Furthermore, we optimize the performance of our methods by partitioning the newly arriving posts spatio-textually and computing bounds for the coverage and diversity of the posts in each partition. The proposed methods are evaluated experimentally using real-world datasets containing geotagged tweets and photos.

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An increasing amount of user-generated content on the Web is geotagged. This often results in the formation of user trails, e.g., sequences of photos, check-ins, or text messages, that users generate while visiting various locations. In this paper, we introduce and study the problem of identifying sets of locations that are strongly associated under social and textual criteria. We say that a location set is associated with a set of keywords if there exists a user with posts around these locations whose textual descriptions cover all keywords. We measure the strength of this association by the number of users with posts that support it. Although the problem reminisces frequent itemset mining, we show that our support measure does not satisfy the necessary anti-monotonicity property, which is used to effectively prune the search space. Nonetheless, by studying the characteristics of the support measure, we are able to devise an efficient approach. We present a basic and two optimized algorithms, exploiting an inverted or a spatio-textual index to increase efficiency. Finally, we conduct an experimental evaluation using geotagged Flickr photos in three major cities. From a qualitative perspective, the results indicate that the introduced type of query returns meaningful and interesting location sets, which are not discovered by other existing approaches. Furthermore, the proposed optimizations and the use of appropriate indexes significantly reduce computation time.

Time series associated with specific locations, such as visitor check-ins or sensor readings, have increased in size and popularity in several domains. Although several works have focused on efficient time series similarity search, there has been limited attention to the inherent challenge that geolocated time series introduce for hybrid queries on both spatial proximity and time series similarity. To efficiently process such queries, we propose a hybrid index, called TSR-tree, which extends the R-tree by introducing appropriate bounds for the time series indexed at each node. This reduces node accesses during query evaluation by simultaneously pruning the search space in the spatial domain and the time series domain while traversing the index. We also present an optimized version, the BTSR-tree, which uses tighter bounds by bundling together similar time series in each node. We describe how these indices can be used to efficiently evaluate different variants of hybrid queries combining spatial and time series filtering or ranking. Finally, we experimentally evaluate our work using real-world datasets from diverse domains, demonstrating a speed-up of 1.5 to 5 times in hybrid query workloads against the baseline R-tree method.

In this paper, we address the problem of continuously maintaining a concise, diversified summary of the contents of a sliding window over a stream of geotagged posts. Selecting posts to include in the summary takes into account both the criteria of coverage and diversity, and the summary is updated dynamically when the window slides. Our proposed strategy provides a trade-off between information quality and performance. An experimental evaluation of our method is presented using two real-world datasets containing spatio-textual posts from Twitter and Flickr.

The amount of crowdsourced geospatial content on the Web is constantly increasing, providing a wealth of information for a variety of location-based services and applications. This content can be analyzed to discover interesting locations in large urban environments which people choose for different purposes, such as for entertainment, shopping, business or culture. In this paper, we focus on the problem of identifying and describing Streets of Interest. Given the road network in a specified area, and a collection of geolocated Points of Interest and photos in this area, our goal is to identify the most interesting streets for a specified category or keyword set, and to allow their visual exploration by selecting a small and spatio-textually diverse set of relevant photos. We formally define the problem and we present efficient algorithms, based on spatiotextual indices and filter and refinement strategies. The proposed methods are evaluated experimentally regarding their effectiveness and efficiency, using three real-world datasets containing road networks, POIs and photos collected from several Web sources.

Large amounts of user-generated content are posted daily on the Web, including textual, spatial and temporal information. Exploiting this content to detect, analyze and monitor events and topics that have a potentially large span in space and time requires efficient retrieval and ranking based on criteria including all three dimensions. In this paper, we introduce a novel type of spatial-temporal-keyword query that combines keyword search with the task of maximizing the spatio-temporal coverage and diversity of the returned top-f results. We first describe a baseline algorithm based on related search results diversification problems. Then, we develop an efficient approach which exploits a hybrid spatial-temporal-keyword index to drastically reduce query execution time. To that end, we extend two state-of-the- art indices for top-f spatio-textual queries and describe how our proposed approach can be applied on top of them. We evaluate the efficiency of our algorithms by conducting experiments on two large, real-world datasets containing geotagged tweets and photos.

Many applications involve queries that combine spatial, temporal and textual filters. In this paper, we address the problem of efficient evaluation of queries that perform spatial, temporal and keyword-based filtering on historical movement data of objects which are additionally associated with textual information in the form of keywords. Our work combines and builds upon concepts and techniques for spatio-temporal and spatio-textual queries, proposing two hybrid indexes for this purpose. An experimental evaluation of the proposed approaches is presented, using real-world datasets from two different types of sources.

Our work focuses around a Web application that retrieves user-generated geospatial content from multiple popular Web sources, and applies schema mapping and entity matching techniques to obtain an integrated dataset. Moreover, density-based clustering of the obtained data is performed to reveal of interest for various data categories. An analysis and overview of the underlying data are also provided by computing various statistics that are visualized in a series of charts. Further data exploration and navigation is enabled via keyword search and faceted browsing. This demonstration covers all the steps of the process, from selecting an area and the sources for data collection, to visualizing and navigating the integrated results.

Analyzing tracking data of various types of moving objects is an interesting research problem with numerous real-world applications. Several works have focused on continuously monitoring the nearest neighbors of a moving object, while others have proposed similarity measures for finding similar trajectories in databases containing historical tracking data. In this work, we introduce the problem of continuously monitoring nearest trajectories. In contrast to other similar approaches, we are interested in monitoring moving objects taking into account at each timestamp not only their current positions but their recent trajectory in a defined time window. We first describe a generic baseline algorithm for this problem, which applies for any aggregate function used to compute trajectory distances between objects, and without any restrictions on the movement of the objects. Using this as a framework, we continue to derive an optimized algorithm for the cases where the distance between two moving objects in a time window is determined by their maximum or minimum distance in all contained timestamps. Furthermore, we propose additional optimizations for the case that an upper bound on the velocities of the objects exists. Finally, we evaluate the efficiency of our proposed algorithms by conducting experiments on three real-world datasets.

This work proposes a novel method that converts movement trajectories into a hierarchical transportation network. It utilizes an improved map construction algorithm on segmented input data based on types of movement. The produced hierarchical road network layers are then combined into a single network. This segmentation addresses the challenges imposed by noisy, low sampling rate trajectories and provides for a mechanism to accommodate automatic map maintenance on updates. An experimental evaluation is conducted using trajectories derived from GPS tracking taxi fleets and utility vehicles in Berlin, Vienna and Athens.

Nowadays, massive amounts of tracking data for various types of moving objects, including vehicles, humans and animals, are becoming available. Analyzing this type of spatio-temporal data is crucial for discovering movement patterns, understanding and forecasting behaviors, and developing novel applications and services. One problem of particular interest is finding objects that move close together with a certain object during some periods of time. In this paper, we focus on finding the k-nearest moving neighbors for a given query object and time interval. We formulate the problem, using a similarity function that takes into consideration both the proximity and the direction of the trajectories, and we firstly present an exact algorithm. Then, we focus on approximate algorithms in order to reduce the execution time, investigating two directions. The first employs line simplification to approximate the compared trajectories, thus reducing the calculations needed to identify the nearest neighbors. The second relies on estimates of prior probabilities derived from trajectory distributions and attempts to achieve a faster approximation of the k-nearest neighbors. A detailed experimental evaluation of the aforementioned algorithms on three real-world datasets is finally presented in order to verify their efficiency and accuracy.

Tagging has become a very common feature in Web 2.0 applications, providing a simple and effective way for users to freely annotate resources to facilitate their discovery and management. Subsequently, tag clouds have become popular as a summarized representation of a collection of tagged resources. A tag cloud is typically a visualization of the top-k most frequent tags in the underlying collection. In this paper, we revisit tag clouds, to examine whether frequency is the most suitable criterion for tag ranking. We propose alternative tag ranking strategies, based on methods for random walk on graphs, diversification,and rank aggregation. To enable the comparison of different tag selection and ranking methods, we propose a set of evaluation metrics that consider the use of tag clouds for search, navigation and recommendations. We apply these tag ranking methods and evaluation metrics to empirically compare alternative tag clouds in a dataset obtained from Flickr, comprising 488,112 tagged photos organized in 451 groups, and 112,514 distinct tags.

Mining frequent subgraph patterns in graph databases is a challenging and important problem with applications in several domains. Recently, there is a growing interest in generalizing the problem to uncertain graphs, which can model the inherent uncertainty in the data of many applications. The main difficulty in solving this problem results from the large number of candidate subgraph patterns to be examined and the large number of subgraph isomorphism tests required to find the graphs that contain a given pattern. The latter becomes even more challenging, when dealing with uncertain graphs. In this paper, we propose a method that uses an index of the uncertain graph database to reduce the number of comparisons needed to find frequent subgraph patterns. The proposed algorithm relies on the apriori property for enumerating candidate subgraph patterns efficiently. Then, the index is used to reduce the number of comparisons required for computing the expected support of each candidate pattern. It also enables additional optimizations with respect to scheduling and early termination, that further increase the efficiency of the method. The evaluation of our approach on three real-world datasets as well as on synthetic uncertain graph databases demonstrates the significant cost savings with respect to the state-of-the-art approach.

We examine different tag ranking strategies for constructing tag clouds to represent collections of tagged objects. The proposed methods are based on random walk on graphs, diversification, and rank aggregation, and they are empirically evaluated on a data set of tagged images from Flickr.

Web service composition enables seamless and dynamic integration of business applications on the web. The performance of the composed application is determined by the performance of the involved web services. Therefore, non-functional, quality of service aspects are crucial for selecting the web services to take part in the composition. Identifying the best candidate web services from a set of functionally-equivalent services is a multi-criteria decision making problem. The selected services should optimize the overall QoS of the composed application, while satisfying all the constraints specified by the client on individual QoS parameters. In this paper, we propose an approach based on the notion of skyline to effectively and efficiently select services for composition, reducing the number of candidate services to be considered. We also discuss how a provider can improve its service to become more competitive and increase its potential of being included in composite applications. We evaluate our approach experimentally using both real and synthetically generated datasets.

As we move from a Web of data to a Web of services, enhancing the capabilities of the current Web search engines with effective and efficient techniques for Web services retrieval and selection becomes an important issue. Traditionally, the relevance of a Web service advertisement to a service request is determined by computing an overall score that aggregates individual matching scores among the various parameters in their descriptions. Two drawbacks characterize such approaches. First, there is no single matching criterion that is optimal for determining the similarity between parameters. Instead, there are numerous approaches ranging from using Information Retrieval similarity metrics up to semantic logic-based inference rules. Second, the reduction of individual scores to an overall similarity leads to significant information loss. Since there is no consensus on how to weight these scores, existing methods are typically pessimistic, adopting a worst-case scenario. As a consequence, several services, e.g., those having a single unrelated parameter, can be excluded from the result set, even though they are potentially good alternatives. In this work, we present a methodology that overcomes both deficiencies. Given a request, we introduce an objective measure that assigns a dominance score to each advertised Web service. This score takes into consideration all the available criteria for each parameter in the request. We investigate three distinct definitions of dominance score, and we devise efficient algorithms that retrieve the top-k most dominant Web services in each case. Extensive experimental evaluation on real requests and relevance sets, as well as on synthetically generated scenarios, demonstrates both the effectiveness of the proposed technique and the efficiency of the algorithms.

The conceptual design of the Extract -- Transform -- Load (ETL) processes is a crucial, burdensome, and challenging procedure that takes places at the early phases of a Data Warehouse project. Several models have been proposed for the conceptual design and representation of ETL processes, but all share two inconveniences: they require intensive human effort from the designers to create them, as well as technical knowledge from the business people to understand them. In a previous work, we have relaxed the former difficulty by working on the automation of the conceptual design leveraging Semantic Web technology. In this paper, we built upon our previous results and we tackle the second issue by investigating the application of natural language generation techniques to the ETL environment. In particular, we provide a method for the representation of a conceptual ETL design as a narrative, which is the most natural means of communication and does not require knowledge of any specific model. We discuss how linguistic techniques can be used for the establishment of a common application vocabulary. Finally, we present a flexible and customizable template-based mechanism for generating natural language representations for the ETL process requirements and operations.

One of the main tasks in the early stages of a data warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the data sources to the data warehouse. In this article, we propose an ontology-based approach to facilitate the conceptual design of the back stage of a data warehouse. A graph-based representation is used as a conceptual model for the datastores, so that both structured and semi-structured data are supported and handled in a uniform way. The proposed approach is based on the use of Semantic Web technologies to semantically annotate the data sources and the data warehouse, so that mappings between them can be inferred, thereby resolving the issue of heterogeneity. Specifically, a suitable application ontology is created and used to annotate the datastores. The language used for describing the ontology is OWL-DL. Based on the provided annotations, a DL reasoner is employed to infer semantic correspondences and conflicts among the datastores, and to propose a set of conceptual operations for transforming data from the source datastores to the data warehouse.

One of the most important tasks performed in the early stages of a data warehouse project is the analysis of the structure and content of the existing data sources and their intentional mapping to a common data model. Establishing the appropriate mappings between the attributes of the data sources and the attributes of the data warehouse tables is critical in specifying the required transformations in an ETL workflow. The selected data model should besuitable for facilitating the redefinition and revision efforts, typically occurring during the early phases of a data warehouse project, and serve as the means of communication between the involved parties. In this paper, we argue that ontologies constitute a very suitable model for this purpose and show how the usage of ontologies can enable a high degree of automation regarding the construction of an ETL design.

A fully automated wrapper for information extraction from Web pages is presented. The motivation behind such systems lies in the emerging need for going beyond the concept of "human browsing". The World Wide Web is today the main "all kind of information" repository and has been so far very successful in disseminating information to humans. By automating the process of information retrieval, further utilization by targeted applications is enabled. The key idea in our novel system is to exploit the format of the Web pages to discover the underlying structure in order to finally infer and extract pieces of information from the Web page. Our system first identifies the section of the Web page that contains the information to be extracted and then extracts it by using clustering techniques and other tools of statistical origin. STAVIES can operate without human intervention and does not require any training. The main innovation and contribution of the proposed system consists of introducing a signal-wise treatment of the tag structural hierarchy and using hierarchical clustering techniques to segment the Web pages. The importance of such a treatment is significant since it permits abstracting away from the raw tag-manipulating approach. Experimental results and comparisons with other state of the art systems are presented and discussed in the paper, indicating the high performance of the proposed algorithm.