With hundreds of millions of users, spreadsheets are one of the most important end-user applications. Spreadsheets are easy to use and allow users great flexibility in storing data. This flexibility comes at a price: users often treat spreadsheets as a poor man's database, leading to creative solutions for storing high-dimensional data. The trouble arises when users need to answer queries with their data. Data manipulation tools make strong assumptions about data layouts and cannot read these ad-hoc databases. Converting data into the appropriate layout requires programming skills or a major investment in manual reformatting. The effect is that a vast amount of real-world data is "locked-in" to a proliferation of one-off formats. We introduce FlashRelate, a synthesis engine that lets ordinary users extract structured relational data from spreadsheets without programming. Instead, users extract data by supplying examples of output relational tuples. FlashRelate uses these examples to synthesize a program in Flare. Flare is a novel extraction language that extends regular expressions with geometric constructs. An interactive user interface on top of FlashRelate lets end users extract data by point-and-click. We demonstrate that correct Flare programs can be synthesized in seconds from a small set of examples for 43 real-world scenarios. Finally, our case study demonstrates FlashRelate's usefulness addressing the widespread problem of data trapped in corporate and government formats.

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FlashRelate: extracting relational data from semi-structured spreadsheets using examples

This chapter contains sections titled: Introduction Probabilistic Machines Vs. Deterministic Machines A More General Class of Machines Appendix Bibliography

Computability by Probabilistic Machines

We consider the family UREC of unambiguous recognizable two-dimensional languages We prove that there are recognizable languages that are inherently ambiguous, that is UREC family is a proper subclass of REC family The result is obtained by showing a necessary condition for unambiguous recognizable languages Further UREC family coincides with the class of picture languages defined by unambiguous 2OTA and it strictly contains its deterministic counterpart Some closure and non-closure properties of UREC are presented Finally we show that it is undecidable whether a given tiling system is unambiguous

/pdf/unambiguous-recognizable-two-dimensional-languages-2rpmuq28ec.pdf

Unambiguous recognizable two-dimensional languages

Multifunction radars (MFRs) are sophisticated sensors with complex dynamical modes that are widely used in surveillance and tracking. This paper demonstrates that stochastic context-free grammars (SCFGs) are adequate models for capturing the essential features of the MFR dynamics. Specifically, MFRs are modeled as systems that ldquospeakrdquo a language that is characterized by an SCFG. The paper shows that such a grammar is modulated by a Markov chain representing radar's policy of operation. The paper also demonstrates how some well-known statistical signal processing techniques can be applied to MFR signal processing using these stochstic syntactic models. We derive two statistical estimation approaches for MFR signal processing-a maximum likelihood sequence estimator to estimate radar's policies of operation, and a maximum likelihood parameter estimator to infer the radar parameter values. Two layers of signal processing are introduced in this paper. The first layer is concerned with the estimation of MFR's policies of operation. It involves signal processing in the CFG domain. The second layer is concerned with identification of tasks the radar is engaged in. It involves signal processing in the finite-state domain. Both of these signal processing techniques are important elements of a bigger radar signal processing problem that is often encountered in electronic warfare applications-the problem of the estimation of the level of threat that a radar poses to each individual target at any point in time.

Syntactic Modeling and Signal Processing of Multifunction Radars: A Stochastic Context-Free Grammar Approach

The theory of two-dimensional languages, generalizing formal string languages, was motivated by problems arising from image processing and models of parallel computing. Weighted automata and series over pictures map pictures to some semiring and provide an extension to a quantitative setting. We establish a notion of a weighted MSO logics over pictures. The semantics of a weighted formula will be a picture series. We introduce weighted 2-dimensional online tessellation automata (W2OTA) extending the common automata-theoretic model for picture languages. We prove that the class of picture series defined by sentences of the weighted logics coincides with the family of picture series that are computable by W2OTA. Moreover, behaviours of W2OTA coincide precisely with the recognizable picture series characterized in [18].

Weighted picture automata and weighted logics

A computational model for tiling recognizable two-dimensional languages

Tiling systems that recognize two-dimensional languages are intrinsically non-deterministic models. We introduce the notion of deterministic tiling system that generalizes deterministic automata for strings. The corresponding family of languages matches all the requirements of a robust deterministic class. Furthermore we show that, differently from the one-dimensional case, there exist many classes between deterministic and non-deterministic families that we separate by means of examples and decidability properties. Â© Springer-Verlag Berlin Heidelberg 2007.

From determinism to non-determinism in recognizable two-dimensional languages

Recognizable two-dimensional languages (REC) are defined by tiling systems that generalize to two dimensions non-deterministic finite automata for strings. We introduce the notion of deterministic tiling system and the corresponding family of languages (DREC) and study its structural and closure properties. Furthermore we show that, in contrast with the one-dimensional case, there exist other classes between deterministic and non-deterministic families that we separate by means of examples and decidability properties.

Deterministic and Unambiguous Families within Recognizable Two-dimensional Languages

Two-dimensional languages can be recognized by tiling systems. A tiling system becomes an effective device for recognition when a scanning strategy on pictures is fixed. We define a Tiling Automaton as a tiling system together with a scanning strategy and a suitable data structure. In this framework it is possible to define determinism, non-determinism and unambiguity. The class of languages accepted by tiling automata coincides with REC family. Tiling automata are able to simulate on-line tessellation automata. Then (deterministic) tiling automata are compared with the other known (deterministic) automata models for recognition of two-dimensional languages. Â© Springer-Verlag Berlin Heidelberg 2007.

Marcella Anselmo

Papers

Unambiguous recognizable two-dimensional languages

A computational model for tiling recognizable two-dimensional languages

From determinism to non-determinism in recognizable two-dimensional languages

Deterministic and Unambiguous Families within Recognizable Two-dimensional Languages

Tiling automaton: A computational model for recognizable two-dimensional languages