The representation of motherhood in contemporary Catalan fiction

This dissertation analyzes four twenty-first-century Catalan novels which present the complex positions occupied by mothers in the last seven decades. Its conceptual framework posits motherhood as both a changing social construction and a political institution in a constant state of flux. In Inma Monsó´s Todo un carácter (2001), Eva Piquer´s Una victoria diferente (2002), Carme Riera´s La mitad del alma (2004), and Najat El Hachmi´s El último patriarca (2008) motherhood is explored as a metaphorical act, a gender-constructing experience, as well as the locus of expression with regard to gender and power relations. During the dictatorship of Francisco Franco (1939–1975), the majority of women were excluded from public spaces, and forced to stay home to care for their husbands and children. Furthermore, the state criminalized abortion, made contraception and divorce illegal, and promoted an ideal of femininity based on silence, sacrifice, and self-denial. The political changes of the late 1970s allowed women greater personal autonomy, and many women writers began to challenge stereotypical views of women’s social roles. Yet in the 70s and 80s, the narratives of Esther Tusquets, Ana María Moix, and Montserrat Roig represent the mother as a repressive figure whom the daughter must reject in order to liberate herself and regain her voice. It is not until the 90s when the novelists Mercedes Abad, Maruja Torres, Carme Riera, Imma Monsó, Eva Piquer, and María Barbal rehumanize the mother figure, recovering their matrilineal heritage. However, far from suggesting a unified trend in representations of motherhood in Catalan fiction, the diverse points of view of the novels under discussion here reveal that differences in attitudes among women authors about mother-daughter conflict are far from resolved. The theoretical background for this dissertation draws mainly on the work of Adrienne Rich, Nancy Chodorow, and Julia Kristeva. It includes psychoanalytic studies as well as sociologically based essays by Anna López Puig, Amparo Acereda, Jacqueline Cruz, Barbara Zecchi, Ángeles de la Concha, and Raquel Osborne, among others.

Scaling geospatial searches in large spatial databases

Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: “Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000”. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to advances in geolocation technologies (e.g., GPS-enabled smartphones) that allow users to associate location data to objects or events. The latter poses potential data ingestion challenges of large data volumes for practical GIS databases. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre-processing task) can be scaled in MapReduce—a widely-adopted parallel programming model for data intensive problems. The evaluation of our algorithms in a Hadoop cluster showed close to linear scalability in building R-tree indexes. Subsequently, we develop efficient algorithms for processing spatial queries with aspatial conditions. Novel techniques for simultaneously indexing spatial with textual and numeric data are developed to that end. Experimental evaluations with real-world, large spatial datasets measured query response times within the sub-second range for most cases, and up to a few seconds for a small number of cases, which is reasonable for interactive applications. Overall, the previous results show that the MapReduce parallel model is suitable for indexing tasks in spatial databases, and the adequate combination of spatial and aspatial attribute indexes can attain acceptable response times for interactive spatial queries with constraints on aspatial data.

Algorithms for efficient transaction management and consistent queries in client -server semantic object-oriented parallel databases

Large read-only or read-write transactions with a large read set and a small write set constitute an important class of transactions used in such applications as data mining, data warehousing, statistical applications, and report generators. Such transactions are best supported with optimistic concurrency, because locking of large amounts of data for extended periods of time is not an acceptable solution. The abort rate in regular optimistic concurrency algorithms increases exponentially with the size of the transaction. The algorithm proposed in this dissertation solves this problem by using a new transaction scheduling technique that allows a large transaction to commit safely with significantly greater probability that can exceed several orders of magnitude versus regular optimistic concurrency algorithms. A performance simulation study and a formal proof of serializability and external consistency of the proposed algorithm are also presented.^ This dissertation also proposes a new query optimization technique (lazy queries). Lazy Queries is an adaptive query execution scheme which optimizes itself as the query runs. Lazy queries can be used to find an intersection of sub-queries in a very efficient way, which does not require full execution of large sub-queries nor does it require any statistical knowledge about the data.^ An efficient optimistic concurrency control algorithm used in a massively parallel B-tree with variable-length keys is introduced. B-trees with variable-length keys can be effectively used in a variety of database types. In particular, we show how such a B-tree was used in our implementation of a semantic object-oriented DBMS. The concurrency control algorithm uses semantically safe optimistic virtual "locks" that achieve very fine granularity in conflict detection. This algorithm ensures serializability and external consistency by using logical clocks and backward validation of transactional queries. A formal proof of correctness of the proposed algorithm is also presented. ^

Monetary cost -efficient query distribution in ISDN -connected heterogeneous databases

Current technology permits connecting local networks via high-bandwidth telephone lines. Central coordinator nodes may use Intelligent Networks to manage data flow over dialed data lines, e.g. ISDN, and to establish connections between LANs. This dissertation focuses on cost minimization and on establishing operational policies for query distribution over heterogeneous, geographically distributed databases. Based on our study of query distribution strategies, public network tariff policies, and database interface standards we propose methods for communication cost estimation, strategies for the reduction of bandwidth allocation, and guidelines for central to node communication protocols. Our conclusion is that dialed data lines offer a cost effective alternative for the implementation of distributed database query systems, and that existing commercial software may be adapted to support query processing in heterogeneous distributed database systems. ^