Efficient Management of Short-Lived Data

Motivated by the increasing prominence of loosely-coupled systems, such as mobile and sensor networks, which are characterised by intermittent connectivity and volatile data, we study the tagging of data with so-called expiration times. More specifically, when data are inserted into a database, they may be tagged with time values indicating when they expire, i.e., when they are regarded as stale or invalid and thus are no longer considered part of the database. In a number of applications, expiration times are known and can be assigned at insertion time. We present data structures and algorithms for online management of data tagged with expiration times. The algorithms are based on fully functional, persistent treaps, which are a combination of binary search trees with respect to a primary attribute and heaps with respect to a secondary attribute. The primary attribute implements primary keys, and the secondary attribute stores expiration times in a minimum heap, thus keeping a priority queue of tuples to expire. A detailed and comprehensive experimental study demonstrates the well-behavedness and scalability of the approach as well as its efficiency with respect to a number of competitors.Comment: switched to TimeCenter latex styl

A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters

Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the 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, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance

The Non-Optimality of Proposed Monetary Policy Rules Under Timeless-Perspective Commitment

Several recent papers have usefully emphasized the inefficiency that arises from discretionary monetary policymaking, relative to optimal policy from a 'timeless perspective,' in macroeconomic models with forward-looking private behavior. The inefficiency in question is in terms of average outcomes of the conditional expectation of a policy objective that reflects the discounted present value of current and future period losses (which involve squared deviations of inflation and output from specified target levels). In the literature, most of the analysis has been conducted in an optimizing model that features a Calvo-Rotemberg price adjustment equation that includes a 'cost-push' shock term. This literature suggests that policy, which keeps inflation equal to a negative multiple of the change in the output gap, is optimal with respect to the criterion mentioned above -- the unconditional expectation of the policymaker's objective function. Results reported here show, however, that this is not the case -- that an alternative policy rule, suggested by the approach of 'policy design' rather than by 'optimal control,' delivers superior results.

English-medium instruction in European higher education: review and future research.

The purpose of this volume has been to give an account of the status of English as a medium of instruction in various political, geographical and ideological contexts: Northern, Southern, Eastern, Western and Central Europe, regions at different stages of EMI implementation. It is our hope that the preceding chapters have given comparative insights into some of the discussions and issues associated with EMI in European higher education. While contributors have investigated a diverse set of empirical, pedagogical and political issues, many issues remain to be addressed in more detail. In these final few pages of the volume, we briefly review some of the main issues that have arisen in the preceding chapters and the broader EMI literature and propose further directions in methodological approaches, areas, and scopes

Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version

We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are used, where the weight of an edge captures some cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions or travel time. It is a precondition to using a graph model for routing that all edges have weights. Weights that capture travel times and GHG emissions can be extracted from GPS trajectory data collected from the network. However, GPS trajectory data typically lack the coverage needed to assign weights to all edges. This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost. A general framework is proposed to solve the problem. Specifically, the problem is modeled as a regression problem and solved by minimizing a judiciously designed objective function that takes into account the topology of the road network. In particular, the use of weighted PageRank values of edges is explored for assigning appropriate weights to all edges, and the property of directional adjacency of edges is also taken into account to assign weights. Empirical studies with weights capturing travel time and GHG emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark) offer insight into the design properties of the proposed techniques and offer evidence that the techniques are effective.Comment: This is an extended version of "Using Incomplete Information for Complete Weight Annotation of Road Networks," which is accepted for publication in IEEE TKD

Time-Scale and Noise Optimality in Self-Organized Critical Adaptive Networks

Recent studies have shown that adaptive networks driven by simple local rules can organize into "critical" global steady states, providing another framework for self-organized criticality (SOC). We focus on the important convergence to criticality and show that noise and time-scale optimality are reached at finite values. This is in sharp contrast to the previously believed optimal zero noise and infinite time scale separation case. Furthermore, we discover a noise induced phase transition for the breakdown of SOC. We also investigate each of the three new effects separately by developing models. These models reveal three generically low-dimensional dynamical behaviors: time-scale resonance (TR), a new simplified version of stochastic resonance - which we call steady state stochastic resonance (SSR) - as well as noise-induced phase transitions.Comment: 4 pages, 6 figures; several changes in exposition and focus on applications in revised versio

Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+

To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly used. However, as they produce huge amounts of data, only simple aggregates are stored. This removes outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing time series with dimensions that exploits correlation in and among time series. Specifically, we propose compressing groups of correlated time series using an extensible set of model types within a user-defined error bound (possibly zero). We name this new category of model-based compression methods for time series Multi-Model Group Compression (MMGC). We present the first MMGC method GOLEMM and extend model types to compress time series groups. We propose primitives for users to effectively define groups for differently sized data sets, and based on these, an automated grouping method using only the time series dimensions. We propose algorithms for executing simple and multi-dimensional aggregate queries on models. Last, we implement our methods in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our evaluation shows that compared to widely used formats, ModelarDB+ provides up to 13.7 times faster ingestion due to high compression, 113 times better compression due to the adaptivity of GOLEMM, 630 times faster aggregates by using models, and close to linear scalability. It is also extensible and supports online query processing.Comment: 12 Pages, 28 Figures, and 1 Tabl

Do Food Prices Affect Food Security? Evidence from the CPS 2002-2006

In this paper, we estimate the effect of food prices on food insecurity for SNAP recipients using data from the Current Population Survey and the recently published Quarterly Food At Home Price Database. We form a local food price index based on amounts of food for a household of four as established by the Thrifty Food Plan. We use an econometric model that accounts for the endogeneity of SNAP receipt to food insecurity and for household-level unobservables. We find that the average effect of food prices on the probability of food insecurity is positive and significant: an increase of one standard deviation in the price of our food basket is associated with an increase in food insecurity of between 1.3 and 2 percentage points for SNAP households. These results are fairly large in terms of the prevalence of food insecurity in our sample. An increase in food insecurity of this magnitude would be about 8 percent of total food insecurity prevalence for the populations in question. These results suggest that indexing SNAP benefits to local food prices could improve its ability to ameliorate the effects of food insecurity.food price, food insecurity, SNAP, discrete factor model, Demand and Price Analysis, Food Security and Poverty, I38,