dataesr/scientific-paragraphs-categorization huggingface dataset:
Baril and Bellahsène introduce the View Model  (Baril, & Bellahsène, 2003) , which is a method capable of querying XML databases. A data model is defined for each view to organize semi-structured data. An XML warehouse, named DAWAX (DAta WArehouse for XML), based on the View Model is also proposed. Hümmer et al. (2003)  propose an approach, named XCube, to model classical data cubes with XML. Nevertheless, this approach focuses on the exchange and the transportation of classical data cubes over networks rather than on multidimensional modeling with XML. The second approach aims at using XML to design data warehouses according to classical multidimensional models such as star and snowflake schemas. XML-star schema  (Pokorný, 2001)  uses Document Type Definitions (DTDs) to explicit dimension hierarchies.
A dimension is modeled as a sequence of DTDs that are logically associated similarly as referential integrity does in relational databases. Golfarelli et al. (1998)  introduce a Dimensional Fact Model represented via Attribute Trees. They also use XML Schemas to express multidimensional models by including relationships with subelements  (Golfarelli et al. 2001). However,  Trujillo et al. (2004)  think that this approach focuses on the presentation of multidimensional XML rather than on the presentation of the structure of the Multidimensional Conceptual Modeling (MCM) itself. They claim that an Object Oriented (OO) standard model is rather needed to cope with all multidimensional modeling properties at both structural and dynamic levels. Trujillo et al. (2004)  provide a DTD model from which valid XML documents are generated to represent multidimensional models at a conceptual level.
OLAP Online Analytical Processing (OLAP) is an efficient technique for explorative data analysis. This trend is obvious, given the popularity of many OLAP systems  (Codd, 1993) , such as Essbase (Arbor Software) or Express (Oracle Corporation) for instance. Based on a multidimensional conceptual view of the data, these systems are especially well-suited to data analysis, and their characteristics are significantly different from those of relational databases. The analysis process concerns basic or aggregated data containing relevant information. OLAP allows to model data in a dimensional way and to observe data from different perspectives. This approach consists of building data cubes (or hypercubes) on which OLAP operations are performed. A data cube is a set of facts described by measures to be observed along analysis axes (dimensions)  (Kimball, 2002; Inmon, 2005; Chaudhuri & Dayal, 1997).
A dimension may be expressed through several hierarchies. Many aggregation levels for measures can be achieved to obtain either summarized or detailed information using OLAP operators. Thus, hierarchies allow sophisticated analyses and data visualization in a multidimensional database  (Jagadish et al. , 1999). However, the classical OLAP operators are only well-suited to numerical data, whereas, in recent years, more and more data sources beyond conventional alphanumerical data have come into being. This phenomenon especially appears when we wish to analyze and aggregate such objects as chemical compounds or protein networks (chem/bio-informatics), 2D/3D objects (spatial/geographic data and pattern recognition), circuits (computer-aided design, simulation), XML data (with loose schemas) and Web activities (human/computer networks and interactions).
XML DATA WAREHOUSING Already standard on the Web, the XML language has also become a standard for representing business data  (Beyer et al. , 2005). It is particularly adapted for modeling complex data originating from heterogeneous sources, particularly from the Web. Moreover, XML query languages such as XQuery help formulate analytical queries that would be difficult to express in a relational system  (Beyer et al. , 2005). In consequence, there has been a clear trend toward XML warehousing for a couple of years. In this trend, we propose to represent complex data from the Web as XML documents. Then, a multidimensional model is designed to obtain an XML data warehouse. Finally, on-line analysis (OLAP) can take place.
This is mainly achieved by extending existing languages such as Microsoft MDX or XQuery with XML-specific operators  (Park et al. , 2005)  or grouping, numbering, aggregation and cube operators  (Beyer et al. , 2005; Wang et al. , 2005; Wiwatwattana et al. 2007) , respectively. In the same frame of mind, some proposals extend existing OLAP clients to incorporate dimensions and measures extracted from external XML sources (such as Web pages) into a data cube  (Pedersen et al. , 2006). Such extensions are exploited as ordinary dimensions and measures, and allow handling unexpected or short time data requirements. They involve novel multi-granular data models and query languages that formalize and extend the existing system  (Pedersen et al. , 2006).
To support this choice, we propose a modeling process that achieves complex data integration. We first design a conceptual UML model for a complex object. This UML model is then directly translated into an XML Schema, which we view as a logical model. At the physical level, XML documents that are valid against this logical model may be mapped into a relational, objectrelational or XML-native database. After representing complex data as XML documents, we physically integrate them into an Operational Data Store (ODS), which is a buffer ahead of the actual warehouse. At this stage, it is already possible to mine the stored XML documents directly, e. g. , with XML structure mining techniques. In addition, to further analyze these documents" contents efficiently, it is interesting to warehouse them, i.
Hence, we propose to adopt this architecture to represent XML data warehouses  (Mahboubi et al. , 2009). It is actually the translation of a classical constellation schema into XML. More precisely, as XCube  (Hümmer et al. , 2003) , our reference data warehouse model is composed of the three types of XML documents: dw-model. xml represents warehouse metadata (schema, including dimension hierarchies); a set of facts f. xml documents help store facts, i. e. , dimension references and measure values; and a set of dimension d. xml documents help store dimension member values. Figure  2  represents dw-model. xml"s graph structure. Its root node, DW-model, is composed of dimension and FactDoc nodes. A dimension node defines one dimension, its possible hierarchical levels (Level elements) and attributes (including their types), as well as the path to the corresponding dimensiond.
Algebraic expression of XOLAP operators This last decade"s efforts for formalizing OLAP algebras have helped design a formal framework and well-identified operators. Existing OLAP operators, previously defined in either a relational or multidimensional context, are now being adapted to the data model of XML documents (i. e. , graphs) and enriched with XML-specific operators. However, most existing approaches that aim at XOLAP do not fully satisfy these objectives. Some favor the translation of XML data cubes in relational, and query them with extensions of SQL. Others tend toward multidimensional solutions that exploit XML query languages such as XQuery. However, in terms of algebra, this work only proposes a fairly limited number of operators. As  Wiwatwattana et al. (2007) , we aim at an XML-native solution that exploits XQuery.
For this sake, we use the rich TAX Tree Algebra for XML  (Jagadish et al. , 2001)  to support OLAP operators. We can express in TAX the main usual OLAP operators: cube, rotate, switch, roll-up, drill-down, slice, dice, pull and push  (Hachicha et al. , 2008). By doing so, we significantly expand the number of available XOLAP operators since, up to now, related papers only proposed at most three operators each (always including cube). We have also implemented these XOLAP operators into a software prototype that helps generate the corresponding XQuery code. This querying interface is currently coupled to TIMBER XML-native DBMS, but it is actually independent and could operate onto any other DBMS supporting XQuery. In the next step of our work, we are taking inspiration from the principle of X^3  (Wiwatwattana et al.
Related work: coupling OLAP and data mining The major difficulty when combining OLAP and data mining is that traditional data mining algorithms are mostly designed for tabular datasets organized in individual-variable form. Therefore, multidimensional data are not suited for these algorithms. Nevertheless, a lot of previous studies motivated and proved an interest for coupling OLAP with data mining methods. Ramakrishnan et al. (2007)  discuss a class of new problems and techniques that show great promise for exploratory mining in cube spaces. We distinguish three major approaches in this field. The first approach tries to extend the query language of decision support systems to achieve data mining tasks. The DBMiner system, proposed by  Han (1998) , summarizes this approach. Some extended OLAP operators feature data mining methods such as association, classification, prediction, clustering and sequencing.
Han defines OLAP Mining as a new concept that integrates OLAP technology with data mining techniques and allows performing analyses on different portions and levels of abstraction in a data cube. He also introduces OLAM (On-Line Analytical Mining) as a process for extracting knowledge from multidimensional databases. He expects that, in the future, OLAM will be a natural addition to OLAP technology that enhances the power of multidimensional data analysis. Chen et al. (2000)  discover behavior patterns by mining association rules about customers from transactional e-commerce data. They extend OLAP functions and use a distributed OLAP server with a data mining infrastructure. The resulting association rules are represented in particular cubes called Association Rule Cubes. Goil & Choudhary (1998)  think that dimension hierarchies can be used to provide interesting information at multiple concept levels.
Their approach summarizes information in a data cube, extends OLAP operators and mines association rules. Some other researches consist of integrating mining functions in the database system using SQL. Chaudhuri et al. (1999)  propose a data mining system based on extending SQL and develop a client-server middleware that performs a decision tree classifier in MS SQL Server 7. 0. The second approach consists of adapting multidimensional data inside or outside the database system and applies classical data mining algorithms on the resulting datasets. This approach can be viewed with respect to two strategies. The first one consists of taking advantage from multidimensional database management systems (MDBMS) to help construct learning models. The second strategy transforms multidimensional data and makes them usable by data mining methods.
For instance,  Pinto et al. (2001)  integrate multidimensional information in data sequences and apply on them frequent pattern discovery. In order to apply decision trees on multidimensional data,  Goil & Choudhary (1998)  flatten data cubes and extract a contingency matrix for each dimension at each construction step of the tree. Chen et al. (2001)  think that OLAP should be adopted as a pre-processing step in the knowledge discovery process. In the same context,  Maedche et al. (2000)  combine databases with classical data mining systems by using OLAP engine as interface to process telecommunication data. In this interface, OLAP tools create a target data set to generate new hypotheses by applying data mining methods. Tjioe & Taniar (2005)  propose a method for mining association rules in data warehouses.
Parsaye (1997)  claims that decisionsupport applications must consider data mining within multiple dimensions. He proposes a theoretical OLAP Data Mining System that integrates a multidimensional discovery engine in order to perform discovery along multiple dimensions. Sarawagi et al. (1998)  propose to integrate a multidimensional regression module, called Discovery-driven, in OLAP servers. This module guides the user to detect relevant areas at various hierarchical levels of a cube. Imielinski et al. (2002)  propose a generalized version of association rules called Cubegrades. The authors claim that association rules can be viewed as the change of an aggregate's measure due to a change in the cube's structure. Dong et al. (2001)  enhance Cubegrades and introduce constrained gradient analysis.
Personalization Personalization has been mentioned for many years in various domains such as information retrieval and databases. In these domains, personalization usually consists of exploiting user preferences to provide pertinent answers to users. More precisely, in the context of databases, personalization mainly takes the form of adding predicates to queries  (Kießling, 2002). In the context of information retrieval, the idea consists of representing user profile with keywords  (Domshlak & Joachims, 2007)  in order to define a restricted research area. In these domains, personalization then consists of finding pertinent answers within a profusion of data. Research studies about personalization in data warehouses are recent and constitute an emerging trend. The first work is inspired from the concept of restriction, particularly focused on data visualization and user preference-driven navigation.
A user-driven approach for complex data analysis Since our R-DW model is independent from any implementation, we propose to exploit R-DW to deal with complex data. More precisely, we propose to adapt and apply our approach to complex data warehouses represented in XML format. Thus, we consider the XML data warehouse model presented in a previous section. Because of the specificities of XML storing, XML data warehouse evolution is not an easy task. Indeed, we have to consider that structure and data are mixed in documents, even if the data warehouse schema is represented in dw-model. xml. Moreover, we have to take the organization of information as a tree into account. The personalization process consists of creating a new level within a given hierarchy. It implies not only creating this level with required data, but also the links with (an)other level(s).
Moreover, we have to deal with both structure and data. To achieve this evolution in the XML data warehouse, we have to consider the evolution of two documents: the document representing the data warehouse model (dw-model. xml) and the document of the dimension that is concerned by level creation (dimensiond. xml). The evolution process is based on the use of "if-then" rules defined by the user. For a given structure rule and the set of corresponding data-rules, we are building one granularity level. The interest of the structure-rule is to determine the structure of the level. Thus, we can modify the dw-model. xml document by adding a Level node. The dimensiond. xml document concerned by the evolution of the hierarchy is also modified to include the new level with the various properties and the links with the existing level @Drill-Down if the level is added at the end of a hierarchy, @Drill-Down and @Roll-Up if the new level is inserted between two existing ones.
The implementation requires updates of XML documents. First, a Web interface allows user interaction. The interface helps user define graphically its structure rule and data rules. This interaction is developed using PHP scripts. The evolution of the XML data warehouse requires updates within XML documents that are used to store the data warehouse. Xupdate is a lightweight XML query language for modifying XML data. It is specified by the XML:DBInitiative. It is a simple XML update language used to modify XML content by simply declaring what changes should be made in XML syntax. Various operations can be combined to achieve our evolution, such as inserting an element after, inserting an attribute, updating attribute, etc. Another possibility is to consider the use of DOM (Document Object Model) to achieve the evolution.
Indeed, DOM is a platform and language-independent standard object model for representing HTML or XML documents. Thus, it is possible to use PHP scripts with DOM. As an example, let us consider the study we made with our linguist colleagues. The token frequencies were observed with respect to location in the transcription and speaker sex. The initial data warehouse has been designed according to the CLAPI database and the identified analysis needs. Now, let us suppose that a linguist needs to aggregate frequencies by grouping some locations together. For instance, he wants to know where some tokens appear in the transcription (beginning, middle, and end). Even if this need has not been initially expressed, our personalization process allows for evolving the data warehouse to provide an answer.
CONCLUSION We presented in this chapter a complete solution to warehouse complex data from the Web. Even if much recent research has focused on the design of multidimensional models  (Luján-Mora et al. , 2006) , only few research work addresses the issues of managing and analyzing complex data. First, we defined a reference XML data warehouse model that unifies and generalizes existing, similar schemas from the literature. This model explicitly takes into account complex features that are possible in XML, but would be intricate to implement in a relational system, such as complex hierarchies and irregular facts. To provide semantics to OLAP operators, we extend the capabilities of OLAP to description, clustering and explanation by coupling OLAP with data mining techniques. The AHC method is used to define a new OLAP aggregation operator to analyze complex data.
To improve navigation into data cubes, we also used the MCA method to reorganize facts inside the data cube by identifying interesting facts. Finally, to help explanation from multidimensional data, we used frequent association rule mining to extract relationships and associations between facts. To design and build data warehouses, traditional data and goal-driven approaches bear the latent risk of not meeting user requirements. Therefore, user-driven developing approaches seem promising for successful completion of data warehouse projects. In this chapter, we proposed an advanced data warehouse architecture that serves as a modeling framework for user oriented OLAP analyses that take new analysis needs into account. Our main idea is to define a user-oriented evolution of dimension hierarchies in the XML data warehouse.
FUTURE RESEARCH ISSUES The specific characteristics of Web data make the design of BI applications difficult. Such applications help users obtain knowledge from the Web. Content acquisition from the Web can be broken into two phases: information retrieval and information extraction. Web information retrieval is the process of gathering potentially relevant content. Knowledge creation starts from information collected from various sources, then information is normalized and structural and semantic integration must be achieved. One possible solution to facilitate this task is to extract information from the Web, transform and load it into a Web warehouse, which provides uniform access methods for automatic data processing. Web warehousing is conceptually similar to data warehousing approaches used to integrate relational information from databases.
However, the structure of the Web is very dynamic and cannot be controlled by warehouse designers. Web models do not frequently reflect the current state of Web data sources. Thus, Web warehouses must be redesigned at a late stage of development. From this point of view, two promising research issues can be studied to achieve Web data warehousing: First, information retrieval and data mining techniques may be used to collect relevant information from the Web and extract knowledge from them. These techniques are not only useful as analysis support tools, but also as modeling support tools. The semantics extracted from Web data can indeed help build relevant multidimensional models and data cubes. Therefore, relevant analyses can be performed and then presented in a form suitable for use by various individuals playing different roles in an organization.
Acknowledgements We thank  Matthieu Geist ,  Mrinal Kalakrishnan ,  Jonas Buchli ,  Nikolaus Hansen  and  Balázs Kégl  for fruitful discussions and suggestions for improvement. We thank  Stefan Schaal  for providing the source code for running the experiments and tasks in [36]. This work is supported by the  French ANR program MACSi  ( ANR 2010 BLAN 0216 01 ), http://macsi. isir. upmc. fr.
Using a recent software (PhyloType), we explored evolutionary transitions in host-plant genera, feeding sites and geographic distributions in the diversification of Cinara and investigated how transitions in these characters have accompanied speciation events. Results: The diversification of Cinara has been constrained by host fidelity to conifer genera sometimes followed by sequential colonization onto different host species and by feeding-site specialisation. Nevertheless, our analyses suggest that, at the most, only half of the speciation events were accompanied by ecological niche shifts. The contribution of geographical isolation in the speciation process is clearly apparent in the occurrence of species from two continents in the same clades in relatively terminal positions in our phylogeny.
More specifically, we aimed at estimating the relative importance of the two ecological characteristics (host plant species and feeding site) studied by Favret & Voetglin  [30]  versus geographic isolation in the speciation processes. We first investigated how geographic areas, host ranges and feeding sites were distributed along our phylogeny using the recently developed software PhyloType  [34]. Given the distributions of characters across a set of taxa and a phylogeny, PhyloType determines if taxa sharing the same character states are more clustered than expected by chance on the phylogeny. If evolutionary transitions in host plant association and feeding sites were recurrently involved in the diversification of Cinara, sister taxa should diverge for these characters and we should not find any significant phylogenetic clusters associated with these characters.
Buchnera aphidicola is the primary endosymbiont of aphids, which is transmitted from mother to offspring. It has been shown to cospeciate with its aphid hosts at several taxonomic levels in many groups of aphids and its genome has been used with success to reconstruct deep and shallow phylogenetic relationships in aphids  [38] [39] [40]. Polymerase chain reactions (PCR) primers (Additional file 2) were designed for Buchnera DNA fragments using published genomes of Buchnera aphidicola in Genbank. Primer fidelity across taxa was not always consistent in Cytb, His and GroEL, we therefore defined several sets of primers for these DNA fragments. Despite our efforts, some specimens have slightly truncated sequence lengths.
Alignments of the protein coding genes were translated into amino acids using Mega 4. 0. 2 [41]  to detect frameshift mutations and premature stop codons, which may indicate the presence of pseudogenes (i. e. a fragment of nucleotide sequence that resembles a known protein's domains but with stop codons or frameshifts mid-domain). Phylogenetic trees were estimated using maximum likelihood (ML) and Bayesian methods. We first conducted ML searches on each DNA fragment. We checked for topological congruence between the trees and then combined all DNA fragments in a single DNA matrix. All analyses were conducted on a 150-core Linux Cluster at CBGP as well as on the CIPRES Science Gateway  [42]. The data were partitioned into mitochondrial, nuclear and bacterial gene regions following  [38]. The model with best fit for each partition was identified using the Akaike information criterion as implemented in MrAIC.