QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach

Multidimensional query recommendation

In this master thesis we will first summarize the recent efforts to support analytical tasks over relational sources. These efforts have pointed out the necessity to come up with flexible, powerful means for analyzing the issued queries and exploit them in decision oriented processes (such as query recommendation or similar). Issued queries should be decomposed, stored and manipulated in a dedicated subsystem. With this aim, we present a novel approach for representing SQL analytical queries in terms of a multidimensional algebra, which better characterizes the analytical efforts of the user. This thesis discusses how a SQL query can be formulated as a multidimensional algebraic characterization. Then, we discuss how to normalize them in order to bridge (i.e. collapse) several SQL queries into a single characterization (representing the analytical session), according to their logical connections. Afterwards, we talk about how this characterization can be exploited in a wide range of decisional tasks such as query recommendation and others. Finally, we present an implementation example of this approach with limiting it with normalization phase because it surprisingly turned out that it is hard enough to achieve with regards to time available. This implementation may later be upgraded to demonstrate the full potential of this novel approach. 1.3 Scop

A probabilistic model for assigning queries at the edge

Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results

On the Usefulness of SQL-Query-Similarity Measures to Find User Interests

In the sciences and elsewhere, the use of relational databases has become ubiquitous. An important challenge is finding hot spots of user interests. In principle, one can discover user interests by clustering the queries in the query log. Such a clustering requires a notion of query similarity. This, in turn, raises the question of what features of SQL queries are meaningful. We have studied the query representations proposed in the literature and corresponding similarity functions and have identified shortcomings of all of them. To overcome these limitations, we propose new similarity functions for SQL queries. They rely on the so-called access area of a query and, more specifically, on the overlap and the closeness of the access areas. We have carried out experiments systematically to compare the various similarity functions described in this article. The first series of experiments measures the quality of clustering and compares it to a ground truth. In the second series, we focus on the query log from the well-known SkyServer database. Here, a domain expert has interpreted various clusters by hand. We conclude that clusters obtained with our new measures of similarity seem to be good indicators of user interests

Recommending join queries via query log analysis

10.1109/ICDE.2009.122Proceedings - International Conference on Data Engineering964-97

Semantic metadata for supporting exploratory OLAP

Cotutela Universitat Politècnica de Catalunya i Aalborg UniversitetOn-Line Analytical Processing (OLAP) is an approach widely used for data analysis. OLAP is based on the multidimensional (MD) data model where factual data are related to their analytical perspectives called dimensions and together they form an n-dimensional data space referred to as data cube. MD data are typically stored in a data warehouse, which integrates data from in-house data sources, and then analyzed by means of OLAP operations, e.g., sales data can be (dis)aggregated along the location dimension. As OLAP proved to be quite intuitive, it became broadly accepted by non-technical and business users. However, as users still encountered difficulties in their analysis, different approaches focused on providing user assistance. These approaches collect situational metadata about users and their actions and provide suggestions and recommendations that can help users' analysis. However, although extensively exploited and evidently needed, little attention is paid to metadata in this context. Furthermore, new emerging tendencies call for expanding the use of OLAP to consider external data sources and heterogeneous settings. This leads to the Exploratory OLAP approach that especially argues for the use of Semantic Web (SW) technologies to facilitate the description and integration of external sources. With data becoming publicly available on the (Semantic) Web, the number and diversity of non-technical users are also significantly increasing. Thus, the metadata to support their analysis become even more relevant. This PhD thesis focuses on metadata for supporting Exploratory OLAP. The study explores the kinds of metadata artifacts used for the user assistance purposes and how they are exploited to provide assistance. Based on these findings, the study then aims at providing theoretical and practical means such as models, algorithms, and tools to address the gaps and challenges identified. First, based on a survey of existing user assistance approaches related to OLAP, the thesis proposes the analytical metadata (AM) framework. The framework includes the definition of the assistance process, the AM artifacts that are classified in a taxonomy, and the artifacts organization and related types of processing to support the user assistance. Second, the thesis proposes a semantic metamodel for AM. Hence, Resource Description Framework (RDF) is used to represent the AM artifacts in a flexible and re-usable manner, while the metamodeling abstraction level is used to overcome the heterogeneity of (meta)data models in the Exploratory OLAP context. Third, focusing on the schema as a fundamental metadata artifact for enabling OLAP, the thesis addresses some important challenges on constructing an MD schema on the SW using RDF. It provides the algorithms, method, and tool to construct an MD schema over statistical linked open data sets. Especially, the focus is on enabling that even non-technical users can perform this task. Lastly, the thesis deals with queries as the second most relevant artifact for user assistance. In the spirit of Exploratory OLAP, the thesis proposes an RDF-based model for OLAP queries created by instantiating the previously proposed metamodel. This model supports the sharing and reuse of queries across the SW and facilitates the metadata preparation for the assistance exploitation purposes. Finally, the results of this thesis provide metadata foundations for supporting Exploratory OLAP and advocate for greater attention to the modeling and use of semantics related to metadata.El processament analític en línia (OLAP) és una tècnica àmpliament utilitzada per a l'anàlisi de dades. OLAP es basa en el model multi-dimensional (MD) de dades, on dades factuals es relacionen amb les seves perspectives analítiques, anomenades dimensions, i conjuntament formen un espai de dades n-dimensional anomenat cub de dades. Les dades MD s'emmagatzemen típicament en un data warehouse (magatzem de dades), el qual integra dades de fonts internes, les quals posteriorment s'analitzen mitjançant operacions OLAP, per exemple, dades de vendes poden ser (des)agregades a partir de la dimensió ubicació. Un cop OLAP va ser provat com a intuïtiu, va ser ampliament acceptat tant per usuaris no tècnics com de negoci. Tanmateix, donat que els usuaris encara trobaven dificultats per a realitzar el seu anàlisi, diferents tècniques s'han enfocat en la seva assistència. Aquestes tècniques recullen metadades situacionals sobre els usuaris i les seves accions, i proporcionen suggerències i recomanacions per tal d'ajudar en aquest anàlisi. Tot i ésser extensivament emprades i necessàries, poca atenció s'ha prestat a les metadades en aquest context. A més a més, les noves tendències demanden l'expansió d'ús d'OLAP per tal de considerar fonts de dades externes en escenaris heterogenis. Això ens porta a la tècnica d'OLAP exploratori, la qual es basa en l'ús de tecnologies en la web semàntica (SW) per tal de facilitar la descripció i integració d'aquestes fonts externes. Amb les dades essent públicament disponibles a la web (semàntica), el nombre i diversitat d'usuaris no tècnics també incrementa signifícativament. Així doncs, les metadades per suportar el seu anàlisi esdevenen més rellevants. Aquesta tesi doctoral s'enfoca en l'ús de metadades per suportar OLAP exploratori. L'estudi explora els tipus d'artefactes de metadades utilitzats per l'assistència a l'usuari, i com aquests són explotats per proporcionar assistència. Basat en aquestes troballes, l'estudi preté proporcionar mitjans teòrics i pràctics, com models, algorismes i eines, per abordar els reptes identificats. Primerament, basant-se en un estudi de tècniques per assistència a l'usuari en OLAP, la tesi proposa el marc de treball de metadades analítiques (AM). Aquest marc inclou la definició del procés d'assistència, on els artefactes d'AM són classificats en una taxonomia, i l'organització dels artefactes i tipus relacionats de processament pel suport d'assistència a l'usuari. En segon lloc, la tesi proposa un meta-model semàntic per AM. Així doncs, s'utilitza el Resource Description Framework (RDF) per representar els artefactes d'AM d'una forma flexible i reusable, mentre que el nivell d'abstracció de metamodel s'utilitza per superar l'heterogeneïtat dels models de (meta)dades en un context d'OLAP exploratori. En tercer lloc, centrant-se en l'esquema com a artefacte fonamental de metadades per a OLAP, la tesi adreça reptes importants en la construcció d'un esquema MD en la SW usant RDF. Proporciona els algorismes, mètodes i eines per construir un esquema MD sobre conjunts de dades estadístics oberts i relacionats. Especialment, el focus rau en permetre que usuaris no tècnics puguin realitzar aquesta tasca. Finalment, la tesi tracta amb consultes com el segon artefacte més rellevant per l'assistència a usuari. En l'esperit d'OLAP exploratori, la tesi proposa un model basat en RDF per consultes OLAP instanciant el meta-model prèviament proposat. Aquest model suporta el compartiment i reutilització de consultes sobre la SW i facilita la preparació de metadades per l'explotació de l'assistència. Finalment, els resultats d'aquesta tesi proporcionen els fonaments en metadades per suportar l'OLAP exploratori i propugnen la major atenció al model i ús de semàntica relacionada a metadades.On-Line Analytical Processing (OLAP) er en bredt anvendt tilgang til dataanalyse. OLAP er baseret på den multidimensionelle (MD) datamodel, hvor faktuelle data relateres til analytiske synsvinkler, såkaldte dimensioner. Tilsammen danner de et n-dimensionelt rum af data kaldet en data cube. Multidimensionelle data er typisk lagret i et data warehouse, der integrerer data fra forskellige interne datakilder, og kan analyseres ved hjælp af OLAPoperationer. For eksempel kan salgsdata disaggregeres langs sted-dimensionen. OLAP har vist sig at være intuitiv at forstå og er blevet taget i brug af ikketekniske og orretningsorienterede brugere. Nye tilgange er siden blevet udviklet i forsøget på at afhjælpe de problemer, som denne slags brugere dog stadig står over for. Disse tilgange indsamler metadata om brugerne og deres handlinger og kommer efterfølgende med forslag og anbefalinger, der kan bidrage til brugernes analyse. På trods af at der er en klar nytteværdi i metadata (givet deres udbredelse), har stadig ikke været meget opmærksomhed på metadata i denne kotekst. Desuden lægger nye fremspirende teknikker nu op til en udvidelse af brugen af OLAP til også at bruge eksterne og uensartede datakilder. Dette har ført til Exploratory OLAP, en tilgang til OLAP, der benytter teknologier fra Semantic Web til at understøtte beskrivelse og integration af eksterne kilder. Efterhånden som mere data gøres offentligt tilgængeligt via Semantic Web, kommer flere og mere forskelligartede ikketekniske brugere også til. Derfor er metadata til understøttelsen af deres dataanalyser endnu mere relevant. Denne ph.d.-afhandling omhandler metadata, der understøtter Exploratory OLAP. Der foretages en undersøgelse af de former for metadata, der benyttes til at hjælpe brugere, og af, hvordan sådanne metadata kan udnyttes. Med grundlag i disse fund søges der løsninger til de identificerede problemer igennem teoretiske såvel som praktiske midler. Det vil sige modeller, algoritmer og værktøjer. På baggrund af en afdækning af eksisterende tilgange til brugerassistance i forbindelse med OLAP præsenteres først rammeværket Analytical Metadata (AM). Det inkluderer definition af assistanceprocessen, en taksonomi over tilhørende artefakter og endelig relaterede processeringsformer til brugerunderstøttelsen. Dernæst præsenteres en semantisk metamodel for AM. Der benyttes Resource Description Framework (RDF) til at repræsentere AM-artefakterne på en genbrugelig og fleksibel facon, mens metamodellens abstraktionsniveau har til formål at nedbringe uensartetheden af (meta)data i Exploratory OLAPs kontekst. Så fokuseres der på skemaet som en fundamental metadata-artefakt i OLAP, og afhandlingen tager fat i vigtige udfordringer i forbindelse med konstruktionen af multidimensionelle skemaer i Semantic Web ved brug af RDF. Der præsenteres algoritmer, metoder og redskaber til at konstruere disse skemaer sammenkoblede åbne statistiske datasæt. Der lægges særlig vægt på, at denne proces skal kunne udføres af ikke-tekniske brugere. Til slut tager afhandlingen fat i forespørgsler som anden vigtig artefakt inden for bruger-assistance. I samme ånd som Exploratory OLAP foreslås en RDF-baseret model for OLAP-forespørgsler, hvor førnævnte metamodel benyttes. Modellen understøtter deling og genbrug af forespørgsler over Semantic Web og fordrer klargørelsen af metadata med øje for assistance-relaterede formål. Endelig leder resultaterne af afhandlingen til fundamenterne for metadata i støttet Exploratory OLAP og opfordrer til en øget opmærksomhed på modelleringen og brugen af semantik i forhold til metadataPostprint (published version

Extracting schemas for database exploration

Ph.DDOCTOR OF PHILOSOPH